Andreja Avbersek

Sanjay M Sisodiya
email: s.sisodiya@ion.ucl.ac.uk.

Does the primary literature provide support for clinical signs used to distinguish psychogenic nonepileptic seizures from epileptic seizures?

Keywords: psychogenic nonepileptic seizures, ictal signs, video-EEG telemetry. Wordcount: 3512 (excluding abstract, tables and references)

Psychogenic nonepileptic seizures (PNES) represent a diagnostic challenge. When trying to distinguish between PNES and epileptic seizures (ES), clinicians rely on the presence or absence of several clinical signs. Our purpose is to establish the extent to which these signs are supported by primary data from the literature. A Medline search was used to identify primary studies that used video-EEG to define the presence or absence of different clinical signs in PNES and ES. The methodological quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool. 34 studies matched the inclusion criteria. A specific sign was considered well supported by the data from the primary literature if we were able to identify at least two controlled studies demonstrating its usefulness and if the data from other studies were supportive. There is good evidence from the literature that long duration, occurrence from apparent sleep with EEG-verified wakefulness, fluctuating course, asynchronous movements, pelvic thrusting, side-to-side head or body movement, closed eyes during the episode, ictal crying, memory recall and absence of postictal confusion are signs that distinguish PNES from ES. Post-ictal stertorous breathing proved to distinguish convulsive PNES from generalised tonic clonic seizures (GTCS) and should be added to the list of useful clinical signs. The final clinical diagnosis should encompass all available data, and should not rely on any single sign alone.

INTRODUCTION

Psychogenic nonepileptic seizures (PNES) are paroxysmal motor events, disturbances of sensation or of responsiveness that do not result from abnormal electrical activity of the brain, but are caused by a psychological process. In contrast to epileptic seizures (ES), these events lack characteristic electrographic features. [START_REF] Leis | Psychogenic seizures: ictal characteristics and diagnostic pitfalls[END_REF][START_REF] Liske | Pseudoseizures: a problem in the diagnosis and management of epileptic patients[END_REF] A number of PNES types have been described. [START_REF] Oto | Gender differences in psychogenic nonepileptic seizures[END_REF][START_REF] Gröppel | Cluster analysis of clinical seizure semiology to psychogenic nonepileptic seizures[END_REF][START_REF] Bowman | The contribution of life events to pseudoseizure occurrence in adults[END_REF] The most frequently encountered characteristics are excessive movements of limbs, trunk and head. Seizures with stiffening and tremor, as well as seizures with atonia or purely sensory events, are less common. Most PNES are accompanied by an apparent impairment of consciousness. [START_REF] Reuber | Psychogenic nonepileptic seizures: answers and questions[END_REF] When trying to distinguish between PNES and ES, clinicians rely on the presence or absence of several classic signs. In 1881, Gowers described the clinical characteristics of PNES. [START_REF] Gowers | Epilepsy and other chronic convulsive diseases[END_REF] Subsequently, PNES semiology has been the subject of many studies, ranging from case reports to uncontrolled and controlled studies using variable methods from questionnaires to video-EEG. The evidence for all the clinical signs said to distinguish between PNES and ES may thus not be equally strong.

Our purpose is to establish the extent to which the signs said to distinguish between PNES and ES are supported by primary data from the literature.

METHODS

Recent review papers on the semiology of PNES were searched to identify a list of signs commonly used signs to distinguish PNES from ES. [START_REF] Reuber | Psychogenic nonepileptic seizures: answers and questions[END_REF][START_REF] Reuber | Psychogenic nonepileptic seizures: review and update[END_REF][START_REF] Binder | Psychogenic nonepileptic seizures[END_REF][START_REF] Mellers | The approach to patients with "non-epileptic seizures[END_REF][START_REF] Cragar | A review of diagnostic techniques in the differential diagnosis of epileptic and nonepileptic seizures[END_REF] These were: A Medline search was performed to find primary studies between 1980 and June 2009 using the following keywords: psychogenic seizures, pseudoseizures, nonepileptic seizures, psychogenic nonepileptic seizures, clinical signs, ictal signs, video-EEG, telemetry. Abstracts were reviewed to determine which full-text articles should be retrieved. In addition, reference lists from each of the articles that were included in the review were manually searched for papers meeting the inclusion criteria and not identified through Medline. Papers were eligible for inclusion if they assessed one or more of the above mentioned signs, if they used video-EEG as a reference standard to diagnose events, if the frequencies of occurrence of ictal signs were reported for all patient groups or if it was possible to calculate them from the given data, and if the article was in English. Case reports were not included. Studies were excluded if they were conducted on a paediatric population.

The quality of the studies that were included in the review was evaluated using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool (Table 1). [START_REF] Whiting | The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews[END_REF] QUADAS consists of 14 items that are scored as "yes", "no", or "unclear". Although there are no recommendations on scoring, previous studies have used a minimum of 8 or 10 "yes" answers to indicate a study of high quality. [START_REF] Hegedus | Physical examination tests of the shoulder: a systematic review with meta-analysis of individual tests[END_REF][START_REF] Sehgal | Diagnostic utility of facet (zygapophysial) joint injections in chronic spinal pain: a systematic review of evidence[END_REF][START_REF] Shah | Discography as a diagnostic test for spinal pain: a systematic and narrative review[END_REF] Table 1. Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool.

Item

Yes No Unclear 1

Was the spectrum of patients representative of the patients who will receive the test in practice? 2

Were selection criteria clearly described? 3

Is the reference standard likely to correctly classify the target condition? 4

Is the time period between reference standard and index test short enough to be reasonably sure that the target condition did not change between two tests? 5

Did the whole sample or a random selection of the sample, receive verification using a reference standard of diagnosis? 6

Did patients receive the same reference standard regardless of the index test result? 7

Was the reference standard independent of the index test (i.e. the index test did not form part of the reference standard)? 8

Was the execution of the index test described in sufficient detail to permit replication of the test? 9

Was the execution of the reference standard described in sufficient detail to permit its replication? 10

Were the index test results interpreted without knowledge of the results of the reference standard? 11

Were the reference standard results interpreted without knowledge of the results of the index test? 12

Were the same clinical data available when test results were interpreted as would be available when the test is used in clinical practice? 13

Were uninterpretable/intermediate test results reported? 14

Were withdrawals from the study explained? Reproduced from Whiting et al., 2003.[12] Copyright BioMed Central.

RESULTS

A summary of our search procedure is presented in Figure 1. We identified 34 studies that matched the inclusion criteria and also fulfilled 8 or more QUADAS criteria. They are summarised in Table 2. Abbreviations: GTCS=generalised tonic clonic seizures, CPS= complex partial seizures, SMS=supplementary motor partial seizures, TLS=temporal lobe partial seizures, FLPS=frontal lobe partial seizures.

The quality of the studies was heterogeneous: only 22 of them featured a control group of patients with ES; there were also slight differences in selection criteria. Most authors excluded events with subjective phenomena. Three studies (9%) lacked basic demographic information about the patients. None of the studies fulfilled the QUADAS item 7 since assessing the ictal signs was always an integral part of the video-EEG study. The investigators who assessed the ictal signs by viewing video-recordings were blinded to EEG tracings and the results of clinical investigations in 4 (12%) studies. The authors reported the frequencies for ictal signs in two ways; per patient or per event. As this may give slightly different results, we have tried to maintain this distinction throughout the review.

In the following section, we summarise the evidence from primary studies for each of the chosen clinical signs that are used to distinguish PNES from ES. Since the number of the studies meeting the criteria for the review was low, we considered a specific sign well supported by the data from the primary literature if we were able to identify at least two controlled studies proving its usefulness and if the data from other studies were supportive.

Long duration

Seven studies compared duration of PNES and ES. [START_REF] Azar | Postictal breathing pattern distinguishes epileptic from nonepileptic convulsive seizures[END_REF][START_REF] Brown | Characteristics of patients with nonepileptic seizures[END_REF][START_REF] Gates | Ictal characteristics of pseudoseizures[END_REF][START_REF] Henry | Ictal behaviors during nonepileptic seizures differ in patients with temporal lobe interictal epileptiform EEG activity and patients without interictal epileptiform EEG abnormalities[END_REF][START_REF] Jedrzejczak | Psychogenic pseudoepileptic seizures: clinical and electroencephalogram (EEG) video-tape recordings[END_REF][START_REF] Pierelli | Long-term EEG-video-audio monitoring: detection of partial epileptic seizures and psychogenic episodes by 24-hour EEG record review[END_REF][START_REF] Saygi | Frontal lobe partial seizures and psychogenic seizures: comparison of clinical and ictal characteristics[END_REF] According to six of them, the mean duration of PNES was significantly longer than ES duration, irrespective of the type of ES and PNES studied. One study failed to show significant differences in duration -which can probably be attributed to considerable variation in the length of PNES. [START_REF] Gates | Ictal characteristics of pseudoseizures[END_REF] The ranges of duration of the events were reported by four authors.

According to three of them, ES did not exceed 2 minutes in duration, while there was wide variation in the duration of PNES (from less than 1 minute to 150 minutes). [START_REF] Azar | Postictal breathing pattern distinguishes epileptic from nonepileptic convulsive seizures[END_REF][START_REF] Gates | Ictal characteristics of pseudoseizures[END_REF][START_REF] Henry | Ictal behaviors during nonepileptic seizures differ in patients with temporal lobe interictal epileptiform EEG activity and patients without interictal epileptiform EEG abnormalities[END_REF] One study which included patients with partial ES found the maximum duration of an ES to be 275 seconds. [START_REF] Pierelli | Long-term EEG-video-audio monitoring: detection of partial epileptic seizures and psychogenic episodes by 24-hour EEG record review[END_REF] Four studies only measured the duration of PNES. Although the length of PNES within the studies was variable, a mean duration of several minutes was a consistent finding.

PNES lasting less than 1 minute were either not found or occurred very rarely -in 4.5% of the patients in the study by Meierkord et al. [START_REF] Flügel | Closed eyes during a seizure indicate psychogenic etiology: a study with suggestive seizure provocation[END_REF][START_REF] Meierkord | The clinical features and prognosis of pseudoseizures diagnosed using video-EEG telemetry[END_REF][START_REF] Raymond | Video-EEG telemetry: apparent manifestation of both epileptic and non-epileptic attacks causing potential diagnostic pitfalls[END_REF][START_REF] Silva | Clinical features and prognosis of nonepileptic seizures in a developing country[END_REF] There is considerable evidence to suggest that a duration of more than 2 minutes is highly suggestive of PNES, although this is an arbitrary limit. Partial ES may last longer than 2 minutes and PNES occasionally do not exceed one minute in duration.

Occurrence from sleep

In three controlled studies, all PNES occurred from EEG-verified wakefulness, but the occurrence of ES from sleep was common (31-59% of events). [START_REF] Bazil | Effects of sleep and sleep stage on epileptic and nonepileptic seizures[END_REF][START_REF] Pierelli | Long-term EEG-video-audio monitoring: detection of partial epileptic seizures and psychogenic episodes by 24-hour EEG record review[END_REF][START_REF] Saygi | Frontal lobe partial seizures and psychogenic seizures: comparison of clinical and ictal characteristics[END_REF] Benbadis et al.

also studied preictal pseudosleep (PIPS), when the patient appears to be asleep but EEG shows normal activity of wakefulness. Although 23% of PNES events in 54% of patients occurred from PIPS, this was never observed in ES (p<0.01). [START_REF] Benbadis | Preictal pseudosleep: a new finding in psychogenic seizures[END_REF] Four uncontrolled studies included this parameter. None of the patients was observed to have PNES during sleep. [START_REF] Raymond | Video-EEG telemetry: apparent manifestation of both epileptic and non-epileptic attacks causing potential diagnostic pitfalls[END_REF][START_REF] Thacker | Nonepileptic seizures during apparent sleep[END_REF] Occurrence from PIPS was found in 12% to 39% of PNES patients. [START_REF] Hovorka | Psychogenic non-epileptic seizures, prospective clinical experience: diagnosis, clinical features, risk factors, psychiatric comorbidity, treatment outcome[END_REF][START_REF] Thacker | Nonepileptic seizures during apparent sleep[END_REF] Of note, Orbach et al. analysed PNES events that apparently occurred during sleep. Most of them occurred from PIPS, but 7% of events occurred either during EEGverified sleep or within 7 seconds after the onset of alpha-rhythm. [START_REF] Orbach | Psychogenic, nonepileptic seizures associated with video-EEG-verified sleep[END_REF] In conclusion, there is good evidence to suggest that the occurrence of seizures from sleep can distinguish ES from PNES. The diagnosis of sleep itself in these circumstances will require EEG.

Gradual onset

One study compared the onset of PNES and ES. A gradual buildup of visible signs was seen significantly more often in partial ES (81%) than in PNES (6.3%, p<0.01). [START_REF] Chen | Sensitivity and specificity of video alone versus electroencephalography alone for the diagnosis of partial seizures[END_REF] One uncontrolled study found that in 40% of patients with PNES with bilateral motor activity, motor activity began gradually. [START_REF] Gulick | Pseudoseizures: ictal phenomena[END_REF] There is insufficient evidence to support this clinical sign.

Fluctuating course

This parameter was assessed in two controlled clinical studies. waxing-waning event tempo was seen in 69% of PNES and only 3.7% of ES (p<0.01). [START_REF] Chen | Sensitivity and specificity of video alone versus electroencephalography alone for the diagnosis of partial seizures[END_REF] In one uncontrolled study, 8 patients with several recorded PNES were identified. In 7 of them the episodes occurred in clusters with brief intervening periods during which the patient usually remained unresponsive. [START_REF] Gulick | Pseudoseizures: ictal phenomena[END_REF] There is thus sufficient evidence from the literature to suggest that fluctuating course distinguishes PNES from partial and generalised ES.

Non-stereotyped attacks

Only uncontrolled studies were identified in this category. 160 patients with PNES were included in the most extensive study: 88% of them had stereotyped attacks. [START_REF] Oto | Gender differences in psychogenic nonepileptic seizures[END_REF] In the remaining four studies, stereotyped attacks were found in 77% to 96% of PNES patients. [START_REF] Gröppel | Cluster analysis of clinical seizure semiology of psychogenic nonepileptic seizures[END_REF][START_REF] Gulick | Pseudoseizures: ictal phenomena[END_REF][START_REF] Raymond | Video-EEG telemetry: apparent manifestation of both epileptic and non-epileptic attacks causing potential diagnostic pitfalls[END_REF][START_REF] Silva | Clinical features and prognosis of nonepileptic seizures in a developing country[END_REF] There is insufficient evidence to suggest that non-stereotyped attacks are a feature of PNES. Furthermore, there is some evidence from uncontrolled studies that PNES events show consistent semiology with little variation in different episodes.

Flailing or thrashing movements

Thrashing and writhing movements were observed in 17% of partial ES and 31% of PNES in one controlled study. The difference was not statistically significant. [START_REF] Chen | Sensitivity and specificity of video alone versus electroencephalography alone for the diagnosis of partial seizures[END_REF] In two uncontrolled studies, flailing or thrashing movements were observed in 18% and 19% of the patients with PNES. [START_REF] Geyer | The value of pelvic thrusting in the diagnosis of seizures and pseudoseizures[END_REF][START_REF] Gulick | Pseudoseizures: ictal phenomena[END_REF] We therefore did not find sufficient evidence to suggest that the presence of flailing or thrashing movements can distinguish PNES from ES.

Asynchronous movements

This ictal sign was assessed in three controlled studies. ). [START_REF] Chen | Sensitivity and specificity of video alone versus electroencephalography alone for the diagnosis of partial seizures[END_REF] As the authors of two uncontrolled studies included several types of PNES, they found asynchronous limb movements in a smaller proportion of their patients (34% and 9-10%). [START_REF] Leis | Psychogenic seizures: ictal characteristics and diagnostic pitfalls[END_REF][START_REF] Hovorka | Psychogenic non-epileptic seizures, prospective clinical experience: diagnosis, clinical features, risk factors, psychiatric comorbidity, treatment outcome[END_REF] There is sufficient evidence to suggest that the presence of asynchronous limb movements can distinguish convulsive PNES from GTCS and partial ES, with the exception of FLPS.

Pelvic thrusting

Pelvic thrusting is a specific behavioural characteristic that has traditionally been associated with PNES. [START_REF] Reuber | Psychogenic nonepileptic seizures: review and update[END_REF] [START_REF] Devinsky | Clinical profile of patients with epileptic and nonepileptic seizures[END_REF] Importantly, when PNES were compared with FLPS, there was no statistically significant difference in the frequency of pelvic thrusting. [START_REF] Azar | Postictal breathing pattern distinguishes epileptic from nonepileptic convulsive seizures[END_REF][START_REF] Geyer | The value of pelvic thrusting in the diagnosis of seizures and pseudoseizures[END_REF][START_REF] Saygi | Frontal lobe partial seizures and psychogenic seizures: comparison of clinical and ictal characteristics[END_REF] In six uncontrolled studies, pelvic thrusting was present in 7% to 27% of PNES patients. [START_REF] Abubakr | Psychogenic seizures: clinical features and psychological analysis[END_REF][START_REF] Flügel | Closed eyes during a seizure indicate psychogenic etiology: a study with suggestive seizure provocation[END_REF][START_REF] Gröppel | Cluster analysis of clinical seizure semiology of psychogenic nonepileptic seizures[END_REF][START_REF] Gulick | Pseudoseizures: ictal phenomena[END_REF][START_REF] Hovorka | Psychogenic non-epileptic seizures, prospective clinical experience: diagnosis, clinical features, risk factors, psychiatric comorbidity, treatment outcome[END_REF][START_REF] Luther | Pseudoepileptic seizures: methods and video analysis to aid diagnosis[END_REF] We therefore conclude that there is sufficient evidence from the primary studies to suggest that the presence of pelvic thrusting can distinguish between convulsive PNES and GTCS, but not between PNES and FLPS.

Opisthotonus, 'arc en cercle'

Only two uncontrolled studies reported on this parameter. Opisthotonus was present in 19% and 29% of PNES patients, respectively. [START_REF] Gulick | Pseudoseizures: ictal phenomena[END_REF][START_REF] Hovorka | Psychogenic non-epileptic seizures, prospective clinical experience: diagnosis, clinical features, risk factors, psychiatric comorbidity, treatment outcome[END_REF] Due to the lack of controlled studies, there is not enough evidence to support this clinical sign.

Side-to-side head or body movement

Five controlled studies reported on this variable. In both of the studies that compared convulsive PNES with GTCS, the proportion of patients (or events) with side-to-side head or body turning was significantly higher in the PNES groups (p<0.01). and none of the ES events (p<0.05). [START_REF] Chen | Sensitivity and specificity of video alone versus electroencephalography alone for the diagnosis of partial seizures[END_REF] However, when PNES were compared with complex partial PNES, side-to-side head turning occurred in 20% of patients in both groups. [START_REF] Pierelli | Long-term EEG-video-audio monitoring: detection of partial epileptic seizures and psychogenic episodes by 24-hour EEG record review[END_REF] Similarly, there was no statistically significant difference between PNES and FLPS. [START_REF] Azar | Postictal breathing pattern distinguishes epileptic from nonepileptic convulsive seizures[END_REF][START_REF] Saygi | Frontal lobe partial seizures and psychogenic seizures: comparison of clinical and ictal characteristics[END_REF] In three uncontrolled studies, side-to-side head or body turning was observed in 15% to 23% of patients. [START_REF] Leis | Psychogenic seizures: ictal characteristics and diagnostic pitfalls[END_REF][START_REF] Hovorka | Psychogenic non-epileptic seizures, prospective clinical experience: diagnosis, clinical features, risk factors, psychiatric comorbidity, treatment outcome[END_REF][START_REF] Luther | Pseudoepileptic seizures: methods and video analysis to aid diagnosis[END_REF] There is sufficient evidence from the primary studies to suggest that the presence of side-toside head or body movement can distinguish between convulsive PNES and GTCS, but not between PNES and some other types of ES.

Closed eyes

This sign was assessed in five controlled studies. When events were considered, ictal eye closure was significantly more frequent in PNES (34-87%) than ES (0-26%). [START_REF] Azar | Postictal breathing pattern distinguishes epileptic from nonepileptic convulsive seizures[END_REF][START_REF] Chen | Sensitivity and specificity of video alone versus electroencephalography alone for the diagnosis of partial seizures[END_REF][START_REF] Detoledo | Patterns of involvement of facial muscles during epileptic and nonepileptic events: review of 654 events[END_REF][START_REF] Syed | Do observer and self-reports of ictal eye closure predict psychogenic nonepileptic seizures?[END_REF] 96% of PNES patients and only 2.6% of ES patients kept their eyes closed during the ictal phase in the study by Chung et al. [START_REF] Chung | Ictal eye closure is a reliable indicator for psychogenic nonepileptic seizures[END_REF] Ictal eye closure was also found in a high proportion of PNES patients (52-90%) in three uncontrolled studies. [START_REF] Flügel | Closed eyes during a seizure indicate psychogenic etiology: a study with suggestive seizure provocation[END_REF][START_REF] Gulick | Pseudoseizures: ictal phenomena[END_REF][START_REF] Hovorka | Psychogenic non-epileptic seizures, prospective clinical experience: diagnosis, clinical features, risk factors, psychiatric comorbidity, treatment outcome[END_REF] There is good evidence from the primary literature to suggest that closed eyes during an attack can distinguish PNES from ES.

Tongue biting

Oliva et al. compared convulsive PNES with generalised ES. No tongue injuries were seen in PNES. Lacerations of the side of the tongue occurred in 11% of ES, and tip of the tongue was injured in one event (0.8%). [START_REF] Oliva | The diagnostic value of oral lacerations and incontinence during convulsive "seizures[END_REF] Three uncontrolled studies examined this parameter. [START_REF] Devinsky | Clinical profile of patients with epileptic and nonepileptic seizures[END_REF][START_REF] Hovorka | Psychogenic non-epileptic seizures, prospective clinical experience: diagnosis, clinical features, risk factors, psychiatric comorbidity, treatment outcome[END_REF][START_REF] Oto | Gender differences in psychogenic nonepileptic seizures[END_REF] In conclusion, there is not enough evidence from controlled studies to support this clinical parameter.

Urinary incontinence

Two controlled studies reported on this clinical sign. In the first one, incontinence was observed in 23% of epilepsy patients and 6% of PNES patients. The difference was not statistically significant (p=0.09). [START_REF] Oliva | The diagnostic value of oral lacerations and incontinence during convulsive "seizures[END_REF] In the study by Slater et al., none of the PNES patients was incontinent during the attack, while this was observed in 26% of ES patients (p<0.01). [START_REF] Slater | Induction of pseudoseizures with intravenous saline placebo[END_REF] Three of the five uncontrolled studies failed to document incontinence in PNES patients. [START_REF] Devinsky | Clinical profile of patients with epileptic and nonepileptic seizures[END_REF][START_REF] Hovorka | Psychogenic non-epileptic seizures, prospective clinical experience: diagnosis, clinical features, risk factors, psychiatric comorbidity, treatment outcome[END_REF][START_REF] Luther | Pseudoepileptic seizures: methods and video analysis to aid diagnosis[END_REF] It was seen in 6% of patients with PNES in the study by Silva et al., in 21% of males and 33% of females in the study by Oto et al. [START_REF] Oto | Gender differences in psychogenic nonepileptic seizures[END_REF][START_REF] Silva | Clinical features and prognosis of nonepileptic seizures in a developing country[END_REF] There is not enough evidence to support the usefulness of this clinical sign in distinguishing PNES from ES. Furthermore, urine incontinence can occur in syncope.

Ictal crying

Four controlled studies assessed this parameter. In the study by found it in 21% of males and 43% of females. [START_REF] Gulick | Pseudoseizures: ictal phenomena[END_REF][START_REF] Hovorka | Psychogenic non-epileptic seizures, prospective clinical experience: diagnosis, clinical features, risk factors, psychiatric comorbidity, treatment outcome[END_REF][START_REF] Oto | Gender differences in psychogenic nonepileptic seizures[END_REF] In conclusion, there is sufficient evidence to suggest that ictal crying is rather specific for PNES, although its sensitivity seems low.

Recall for the period when the patient appears unconscious

Two authors tested memory recall for the ictal period. Bell et al. performed ictal cognitive assessment in 13 patients with PNES and 31 patients with complex partial seizures (CPS).

The recall of memory items never reached 50% in CPS, but was more than 50% in 54% of PNES (p<0.01). If any memory recall was considered as a parameter, the sensitivity was 63% with 96% specificity for PNES. [START_REF] Bell | Ictal cognitive assessment of partial seizures and pseudoseizures[END_REF] In the study by Devinsky et al., memory recall was tested in 16 patients in a group of patients with both PNES and ES. 88% of them were able to recall items presented to them during the ictus after their PNES, compared with only 6.3% of patients after their ES. Similarly, 85% of patients with only PNES recalled the items, as opposed to 10% of patients with only ES. [START_REF] Devinsky | Clinical profile of patients with epileptic and nonepileptic seizures[END_REF] There is sufficient evidence to suggest that memory recall of items presented during the event can distinguish PNES from ES.

Post-ictal recovery of responsiveness

Several studies that assessed post-ictal states used different definitions and consequently we were not able to integrate all the data in this section. [START_REF] Devinsky | Clinical profile of patients with epileptic and nonepileptic seizures[END_REF][START_REF] Gulick | Pseudoseizures: ictal phenomena[END_REF][START_REF] Henry | Ictal behaviors during nonepileptic seizures differ in patients with temporal lobe interictal epileptiform EEG activity and patients without interictal epileptiform EEG abnormalities[END_REF][START_REF] Luther | Pseudoepileptic seizures: methods and video analysis to aid diagnosis[END_REF][START_REF] Saygi | Frontal lobe partial seizures and psychogenic seizures: comparison of clinical and ictal characteristics[END_REF] We can therefore conclude that there is sufficient evidence from the primary literature to suggest that the presence of post-ictal confusion distinguishes ES from PNES.

Post-ictal stertorous breathing

We identified three controlled studies that evaluated this clinical sign and met the QUADAS criteria. Sen et al. and Azar et al. found stertorous breathing after 91% and 61% of GTCS, respectively, while none of the patients was judged to have stertorous breathing after PNES.

The difference was statistically significant in both cases. [START_REF] Azar | Postictal breathing pattern distinguishes epileptic from nonepileptic convulsive seizures[END_REF][START_REF] Sen | Stertorous breathing is a reliably identified sign that helps in the differentiation of epileptic from psychogenic non-epileptic convulsions: an audit[END_REF] The second study also compared post-ictal breathing in patients with PNES and FLPS with prominent motor activity, but there was no statistically significant difference. A similar result was obtained by Chen et al. who compared PNES and CPS. [START_REF] Chen | Sensitivity and specificity of video alone versus electroencephalography alone for the diagnosis of partial seizures[END_REF] The absence of postictal stertorous breathing is a useful clinical sign to distinguish convulsive PNES from GTCS, but not from partial seizures.

DISCUSSION

There is good evidence from the literature to suggest that long duration, fluctuating course, asynchronous movements, pelvic thrusting, side-to-side head or body movement, closed eyes during the episode, ictal crying, and memory recall are signs that distinguish PNES from ES.

Occurrence of the spells from EEG-verified sleep and post-ictal confusion favour ES and are also well supported by the evidence from the primary studies. Post-ictal stertorous breathing proved to distinguish GTCS from convulsive PNES and should be added to the list of useful clinical signs. These findings are summarised in Table 3.

We have concentrated mostly on motor signs in this review so some of the conclusions only apply to spells with predominantly motor manifestations. Patients with PNES with pure sensory phenomena or unresponsiveness were often excluded from the primary studies and consequently information on these two types of PNES is scarce. Other potentially useful clinical signs such as ictal stuttering and the »teddy bear sign« can be found in the literature; some of them have only been described by single authors and have not been included in our review. [START_REF] Vossler | Ictal stuttering: a sign suggestive of psychogenic nonepileptic seizures[END_REF][START_REF] Burneo | Teddy bears: an observational finding in patients with non-epileptic events[END_REF] One of the criteria for inclusion was observation of events in the video-EEG monitoring unit. Consequently, our conclusions do not apply to reports by the subjects or witnesses. This is illustrated in the study by Syed et al., who compared observer and selfreport of eye closure with video-EEG findings. They showed that observers did not reliably assess eye closure while the value of self-report remains uncertain. [START_REF] Syed | Do observer and self-reports of ictal eye closure predict psychogenic nonepileptic seizures?[END_REF] Assessing the primary studies with the QUADAS tool revealed several methodological shortcomings. Only 21 of the studies included in the review featured a control group of patients with ES. Classification of ES was provided in only 12 of them and only a few authors tried to compare patients with PNES that resembled a certain type of ES with that particular type of ES. The investigators who assessed the ictal signs by viewing video-recordings were only rarely blinded to EEG tracings and the results of clinical investigations. Often the clinical signs (for instance post-ictal states, ictal crying) were not defined well enough for adequate comparison with other studies. Several methodological issues were also stressed in a recent review on clinical signs in PNES. [START_REF] Syed | Do observer and self-reports of ictal eye closure predict psychogenic nonepileptic seizures?[END_REF] The authors suggested improvements for future research: prospective design, well-defined clinical signs, inclusion of all types of events, independent assessors blinded to the video-EEG recording. [START_REF] Hoerth | Clinical predictors of psychogenic nonepileptic seizures: a critically appraised topic[END_REF] Our conclusions, examining a broader range of signs, are similar, emphasising the need for more studies.

Another limitation to our review is that all the included studies were carried out in specialised epilepsy centres on adult patients with refractory seizures or spells that presented a diagnostic problem. Generalisability to community-based populations or children can therefore not be assumed.

In conclusion, the level of evidence supporting specific ictal signs used to distinguish between PNES and ES is variable. The same applies to their frequency and specificity for either PNES or ES.

The diagnosis of PNES requires careful integration of history, ictal signs and other clinical and investigational information, and should not be driven by any one clinical sign alone.

Video-EEG monitoring may be crucial for the analysis of ictal characteristics and post-ictal behaviour. Some of the useful clinical signs, for instance post-ictal stertorous breathing, can be reliably identified by trained staff even without telemetry. [START_REF] Sen | Stertorous breathing is a reliably identified sign that helps in the differentiation of epileptic from psychogenic non-epileptic convulsions: an audit[END_REF] Table 3 Note: The sensitivity and specificity values were calculated from the frequencies of clinical signs in PNES and ES. We were not able to obtain the confidence intervals in most cases.

11 .

 11 Recall for the period when the patient appears unconscious 12. Rapid post-ictal recovery of responsiveness

Figure 1 .

 1 Figure 1. Flow diagram of the literature screening process.

Table 2 . Video-EEG studies of ictal features of PNES. Number of patients (events) Study PNES ES

 2

	Abubakr et al., 2003.[16]	23 patients	/
	Azar et al., 2008.[17]	16 patients (24 events)	15 patients with GTCS (23
			seizures)
			9 patients with FLPS (21 seizures)
	Bazil and Walczak, 1997.[18]	280 events	622 CPS, 149 FLPS
	Bell et al., 1998.[19]	13 patients (24 events)	31 patients with CPS (102
			seizures)
			5 patients with SPS (13 seizures)
	Benbadis et al., 1996.[20]	18 patients (68 events)	39 patients (220 seizures)
	Brown et al., 1991.[21]	23 patients	25 patients
	Chen et al., 2008.[22]	16 patients (16 events)	27 patients with simple and
			secondarily generalised partial
			seizures PS (27 events)
	Chung et al., 2000.[23]	151 events	314 seizures
	DeToledo and Ramsay, 1996.[24]	197 events	457 seizures
	Devinsky et al., 1996.[25]	20 patients with PNES only, 20	20 patients
		patients with PNES and ES
	Flugel et al., 1996.[26]	100 patients (100 events)	/
	Gates et al., 1985.[27]	25 patients with convulsive PNES	25 patients with GTCS
	Geyer et al., 2000.[28]	100 patients	100 patients with TLS
			50 patients with FLPS
			11 patients with generalised
			seizures
	Groppel et al., 2000.[29]	27 patients	/
	Gulick et al., 1982.[30]	27 (71 events)	/
	Henry and Drury, 1998.[31]	44 patients	133 patients with complex partial
			or secondarily generalised seizures
	Hovorka et al., 2007.[32]	56 patients	/
	Jedrzejczak et al., 1999.[33]	30 patients with PNES, 25 patients	15 patients (74 seizures)
		with both PNES and ES (221
		PNES events, 9 ES events)
	Leis et al., 1992.[1]	47 patients	/
	Luther et al., 1982.[34]	30 patients (37 events)	/
	Meierkord et al., 1991.[35]	110 patients	/
	Oliva et al., 2008.[36]	18 patients (50 events)	66 patients with primary and

  Gates et al. compared patients with GTCS and convulsive PNES. Asynchronous jerks of upper and lower extremities were seen in 56% of patients with PNES and in none of the patients with ES (p<0.01).[27] Similarly, Azar

	et al. observed asynchronous movements in 96% PNES and only 5% GTCS (p<0.01), but
	there were no differences between PNES and frontal lobe partial seizures (FLPS).[17] Chen et
	al. described asynchronous movements in 44% of PNES events and 7.4% of partial ES. The
	difference was statistically significant (p<0.05

. Summary of evidence that supports the signs used to distinguish between PNES and ES. Sign that favour PNES Evidence from primary studies Sensitivity for PNES Specificity for PNES

	Long duration	Good	/	/
	Fluctuating course	Good	69% (events)	96%
			47-88% (patients)	96-100%
	Asynchronous movements	Good (FLPS excluded)	44-96% (events)	93-96%
			9-56% (patients)	93-100%
	Pelvic thrusting	Good (FLPS excluded)	1-31% (events)	96-100%
			7.4-44% (patients)	92-100%
	Side-to-side head or body	Good (convulsive events	25-63% (events)	96%-100%
	movement	only)	15-36% (patients)	92-100%
	Closed eyes	Good	34-88% (events)	74-100%
			52-96% (patients)	97%
	Ictal crying	Good	13-14% (events)	100%
			3.7-37% (patients)	100%
	Memory recall	Good	63% (events)	96%
			77-88% (patients)	90%
	Signs that favour ES	Evidence from	Sensitivity for ES	Specificity for
		primary studies		ES
	Occurrence from sleep	Good	31-59% (events)	100%
			/	/
	Postictal confusion	Good	61-100% (events)	88%
			67% (patients)	84%
	Stertorous breathing	Good (convulsive events	61-91% (events)	100%
		only)	/	/
	Other signs	Evidence from
		primary studies
	Gradual onset	Insufficient
	Non-stereotyped events	Insufficient
	Flailing or thrashing	Insufficient
	movements
	Opisthotonus, »arc en cercle« Insufficient
	Tongue biting	Insufficient
	Urinary incontinence	Insufficient

Acknowledgements

This work was undertaken at UCLH/UCL who received a proportion of funding from the Department of Health's NIHR Biomedical Research Centres funding scheme.

Copyright licence statement

The Corresponding Author has the right to grant on behalf of all authors and does grant on behalf of all authors, an exclusive licence on a worldwide basis to the BMJ Publishing Group Ltd and its Licensees to permit this article (if accepted) to be published in the Journal of


T Gili

M Cercignani

L Serra

R Perri

F Giove

B Maraviglia

C Caltagirone

M Bozzali
email: m.bozzali@hsantalucia.it

REGIONAL BRAIN ATROPHY AND FUNCTIONAL DISCONNECTION ACROSS ALZHEIMER'S DISEASE EVOLUTION

Keywords: AD, MCI, fMRI, Resting State, Functional Connectivity, VBM

Objective: to assess the contribution of regional grey matter (GM) atrophy and functional disconnection in determining the level of cognitive decline in patients with Alzheimer's disease (AD) at different clinical stages.

Methods

Ten patients with amnesic mild cognitive impairment (a-MCI), 11 patients with probable AD, and 10 healthy controls were recruited. T1-volumes were obtained from each subject and post-processed according to an optimized voxel based morphometry protocol. Resting-state functional MRI data were also collected from the same individuals, and analysed to produce connectivity maps after identification of the default mode network (DMN) by independent component analysis (ICA).

Results: Compared to healthy controls, both AD and a-MCI patients showed a similar regional pattern of brain disconnection between the posterior cingulate cortex (PCC) and the medial prefrontal cortex (mPFC) and the rest of the brain. Conversely, the distribution of GM atrophy was significantly more restricted in a-MCI than in AD patients. Interestingly, the PCC showed reduced connectivity in a-MCI patients in the absence of GM atrophy, which was, in contrast, detectable at the stage of fully developed AD.

Conclusions:

This study indicates that disconnection precedes GM atrophy in the PCC, which is a critical area of the DMN, and supports the hypothesis that GM atrophy in specific regions of AD brains likely reflect a long-term effect of brain disconnection. In this context, our study indicates that GM atrophy in PCC accompanies the conversion from MCI to AD.

Introduction

The clinical presentation of Alzheimer's disease (AD) is typically characterized by an early and prominent impairment of memory functions, followed by a progressive accumulation of additional cognitive deficits, eventually resulting in dementia [START_REF] Petersen | Conceptual overview[END_REF] . A number of structural and functional neuroimaging studies have independently shown inconsistent distributions between structural [START_REF] Baron | In vivo mapping of gray matter loss with voxelbased morphometry in mild Alzheimer's disease[END_REF][START_REF] Matsuda | Longitudinal evaluation of both morphologic and functional changes in the same individuals with Alzheimer's disease[END_REF][START_REF] Karas | A comprehensive study of gray matter loss in patients with Alzheimer's disease using optimized voxel-based morphometry[END_REF] and metabolic changes [START_REF] Minoshima | Metabolic reduction in the posterior cingulate cortex in very early Alzheimer's disease[END_REF][START_REF] Ibanez | Regional glucose metabolic abnormalities are not the result of atrophy in Alzheimer's disease[END_REF][START_REF] Nestor | Limbic hypometabolism in Alzheimer's disease and mild cognitive impairment[END_REF][START_REF] Borroni | Combined 99mTc-ECD SPECT and neuropsychological studies in MCI for the assessment of conversion to AD[END_REF] in AD brains.

Only recently, brain volumetrics and Positron Emission Tomography (PET) were used in combination 9 10 , showing that hypometabolism largely exceeds grey matter (GM) atrophy in most brain regions of AD patients [START_REF] Chételat | Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer's disease[END_REF] . Moreover, a strict relationship between hypometabolism in the posterior cingulate cortex (PCC), hippocampal volume, and disruption of the cingulum bundle was observed [START_REF] Villain | Relationships between hippocampal atrophy, white matter disruption, and gray matter hypometabolism in Alzheimer's disease[END_REF] . These findings suggest that the clinical manifestations of AD are not only associated with regional GM loss, but may also be due to abnormal functional integration of different brain regions. Functional MRI (fMRI) has shown the ability to record spontaneous brain activity fluctuations when subjects lie still in the scanner, at rest. The so-called default-mode network (DMN), which includes the PCC, inferior parietal, and medial prefrontal cortex (mPFC) has been identified as a neuronal network showing a coherent pattern of activation (functional connectivity) [START_REF] Raichle | A default mode of brain function: a brief history of an evolving idea[END_REF] . These regions, which show correlation in their spontaneous fluctuations at rest, are believed to be similarly modulated by cognitive tasks. In this perspective, changes in strength of correlation within regions of the DMN at rest, are expected to express an indirect measure of regional brain disconnection. Voxel-based morphometry (VBM) is a spatially-specific and unbiased method of analysis of MR images reflecting the regional GM volume at a voxel scale [START_REF] Good | A voxel-based morphometric study of ageing in 465 normal adult human brains[END_REF] . This technique has already been successfully applied to AD, showing patterns of GM abnormalities that fit well with the clinical stage of the disease, and also predict the risk of conversion from mild cognitive impairment (MCI) [START_REF] Petersen | Current concepts in mild cognitive impairment[END_REF] , which is a condition associated with an high risk of conversion to AD in short time, to AD 14 15 .

Aim of this study was to investigate, using VBM and resting-state fMRI, the contribution of regional GM atrophy and DMN disconnection in determining the level of cognitive decline in patients with AD at different clinical stages. For this purpose, three different groups of subjects were investigated, including healthy elderly individuals, patients with amnesic MCI (a-MCI), and patients with AD.

Materials and methods

Subjects. Ten patients meeting the diagnostic criteria for MCI [START_REF] Petersen | Conceptual overview[END_REF] and 11 patients with a diagnosis of probable AD [START_REF] Mckhann | Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease[END_REF] attending a specialist dementia clinic were recruited for this study. All a-MCI patients complained of subjective memory loss, with no impact on their daily living activities, which was confirmed by an informant assistant. None of them fulfilled NINCDS-ADRDA consensus criteria [START_REF] Mckhann | Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease[END_REF] for the diagnosis of probable AD, and their Clinical Dementia Rating 17 (CDR) score did not exceed 0.5. Ten healthy subjects (HS), with no complaint of cognitive problems or any evidence of cognitive deficits on neuropsychological testing, were also enrolled in the study and served as control group. The principal demographic data of the studied population are summarized in Table 1. All recruited subjects (patients and controls) underwent an extensive neuropsychological battery testing for several cognitive domains (see below).

Inclusion criteria required that a-MCI patients scored outside the range of normality (<95% of the lower tolerance limit of the distribution from the Italian normative data) in at least one of the memory tests, and performed within the normality cut-off scores in all other cognitive domains, and that HS did not score outside the normal range on any of neuropsychological tests (see below).

Subjects with either two or more hyperintense lesions with a 10 mm diameter or more than eight hyperintense lesions with a diameter between 5 to 9 mm on dual-echo MR images were excluded. Indeed, the presence of vascular pathology might be at least partially responsible for cognitive deficits, making the diagnosis of MCI (on a neurodegenerative basis) or AD less plausible, as well as the condition of normality in healthy controls.

Major systemic, psychiatric and other neurological illnesses were carefully investigated and excluded in all studied subjects. Ethics Committee approval from Santa Lucia Foundation (Prot. CE/AG4/PROG.198-84) and written informed consent (either from the subjects or from their responsible guardians if incapable) were obtained before study initiation.

Patients with a-MCI underwent a clinical and neuropsychological re-assessment (using the same battery employed at baseline) at one year follow-up. Then, they were reclassified in 3 groups: subjects who still had a purely amnesic form of MCI, those who developed multi-domain MCI, and those who converted to dementia.

Neuropsychological assessment. The extensive neuropsychological battery used to assess all recruited subjects included a general cognitive evaluation, using the Mini Mental State Evaluation (MMSE) 18 19 , and tests specific for each cognitive domain: 1) Verbal episodic long-term memory: Immediate and Delayed recall of a 15-Word List 20 , Short Story Recall 21 ; 2) Visuo-spatial episodic long-term memory: Delayed recall of Complex Rey's Figure 22 ; short-term memory: Digit span and Corsi Block Tapping task [START_REF] Orsini | Verbal and spatial immediate memory span: Normative data from 1355 adults and 1112 children[END_REF] ; 3) Language: Naming subtest of Aachener Aphasia test [START_REF] De Blese | L'Aachener Aphasie test I: problemi e soluzioni per una versione italiana del test e per uno studio crosslinguistico dei disturbi afasici[END_REF] MRI acquisition. All subjects underwent an MRI examination, obtained at 3 T (Magnetom Allegra, Siemens, Erlangen, Germany), including the following sequences: 1) dual-echo turbo spin echo [TSE] (TR=6190 ms, TE=12/109 ms, matrix=256x192, FOV=230x172.5, 48 contiguous slices, slice thickness=3 mm, total scan time=4 minutes); 2) 3D Modified Driven Equilibrium Fourier Transform (MDEFT) scan (TR=1338 ms, TE=2,4 ms, TI=910 ms, flip angle=15°, Matrix=256x224x176, in-plane FOV=256x224 mm 2 , slice thickness=1 mm, total scan time=12 minutes); 3) T2*weighted EPI sensitized to BOLD contrast (TR=2080 ms, TE=30 ms, 32 axial slices parallel to AC-PC line, matrix=64x64, pixel size =3x3 mm 2, slice thickness=2.5 mm, flip angle=70°) for resting state fMRI. BOLD EPI images were collected during rest for a 7-min and 20-sec period, resulting in a total of 220 volumes. During this acquisition, subjects were instructed to keep their eyes closed, not to think of anything in particular, and not to fall asleep. FMRI data analysis. All steps of this analysis are schematically reported in Figure 1.

Resting state fMRI data were pre-processed using statistical parametric mapping (Wellcome Department of Imaging Neuroscience; SPM5), and in-house software implemented in Matlab (the MathWorks, Inc.). For each subject the first five volumes of the fMRI series were discarded to allow for T1 equilibration effects. The pre-processing steps included correction for head motion, compensation for slice-dependent time shifts and registration to the T1-weighted high resolution image (SPM5). The functional data were passed through an additional series of processing steps. Several sources of non specific spurious variance were adjusted by including the following factors as nuisance covariates in the regression model: the six parameters obtained by rigid body head motion correction; the signal averaged over whole brain voxels; the signal averaged over cerebral spinal fluid (CSF) voxels; the signal averaged over white matter (WM) voxels. CSF and WM voxels were derived from the correspondent masks obtained by the VBM pre-processing (see below). Then, for each voxel of the fMRI data, temporal filtering was applied to remove constant offsets and linear trends over each run, while retaining frequencies in the 0.009-0.08 Hz band. Data were then normalized into Montreal Neurological Institute (MNI) space coordinates [START_REF] Evans | 3D statistical neuroanatomical models from 305 MRI volumes[END_REF] and smoothed using a 8-mm full-width at half-maximum (FWHM) Gaussian kernel. In the present work, we employed the independent component analysis (ICA) (Group ICA for fMRI toolbox, GIFT, http://icatb.sourceforge.net/) to identify, on a subject by subject basis, regions belonging to the DMN, as previously described [START_REF] Calhoun | A method for making group inferences from functional MRI data using independent component analysis[END_REF][START_REF] Greicius | Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI[END_REF][START_REF] Damoiseaux | Consistent resting-state networks across healthy subjects[END_REF][START_REF] Sorg | Selective changes of resting-state networks in individuals at risk for Alzheimer's disease[END_REF] . Briefly, Group ICA for fMRI toolbox first concatenates the individual data across time, and then produces a computation of subject specific components and time courses. For all subjects grouped together, the toolbox performed the analysis in three steps: (1) data reduction, (2) application of the FastICA algorithm, and (3) back-reconstruction for each individual subject [START_REF] De Luca | fMRI resting state networks define distinct modes of long-distance interactions in the human brain[END_REF] . As expected, the functionally relevant resting state networks included the DMN, characterized by the synchronous oscillation of the PCC, the lateral and mPFC, and the parietal cortex 11 29-31 . Figure 2 (top panel) illustrates the average result obtained by ICA in all subjects. In the estimation of within network variations of functional connectivity we focused on the two regions that showed the highest peak Z-score in the mean spatial map obtained from all subjects together (Z>5), the PCC and mPFC. Then, in each subject scan, we identified these two regions-of-interest (ROIs) by considering, for each of them, a cluster including the 100 voxels that obtained the highest score in each ICA-driven DMN. Finally, these regions were used for the correlation analysis between the DMN and the rest of the brain as previously described 32 33 .

The signal time course of each of the two selected ROIs (belonging to the DMN) was correlated with all voxels included in the brain, thus obtaining correlation maps [START_REF] Vincent | Coherent spontaneous activity identifies a hippocampal-parietal memory network[END_REF] , which highlighted those brain regions intrinsically correlated with the ROIs themselves. The basic procedure [START_REF] Fox | The human brain is intrinsically organized into dynamic, anticorrelated functional networks[END_REF] consisted of computing for each brain voxel the correlation coefficient against the BOLD time series extracted from each considered ROI (seed region). Each correlation map was then converted to normally distributed Z values using Fisher's r-to-Z transformation. As explained below, the correlation maps were used for group analyses.

VBM analysis. The T1-weighted volumes (MDEFT) were pre-processed using the VBM protocol implemented in SPM5, which consists of an iterative combination of segmentations and normalisations to produce a GM probability map [START_REF] Ashburner | Unified segmentation[END_REF] in MNI space for each subject. The templates used for VBM analysis are those provided with SPM5. In order to compensate for compression or expansion which might occur during warping of images to match the template, GM maps were "modulated" by multiplying the intensity of each voxel in the final images by the Jacobian determinants derived from the spatial normalization step [START_REF] Ashburner | Why voxel-based morphometry should be used[END_REF] . Every subject's GM, WM and CSF volumes were recorded for further analysis. All data were then smoothed using a 12-mm FWHM Gaussian kernel.

As explained in the next section, modulated and smoothed GM maps were used for group analyses.

Voxelwise statistics for resting state fMRI and VBM. PCC and mPFC driven Z-score maps were respectively included in two second level full factorial statistical analyses in SPM5. Age, education and total GM volumes were entered as covariates of no interest.

For each analysis (PCC and mPFC driven Z-score maps), an F-contrast (p=0.001) was first performed to assess the global pattern of changes across all groups (effects of interest). Then, within the whole pattern of functional changes, between groups Tcontrasts were used to assess the distribution of functional changes at the stages of a-MCI and AD (post-hoc analyses). In all T contrasts (AD vs. HS; aMCI vs. HS; aMCI vs. AD) changes were considered as statistically significant at p values<0.001 cluster level uncorrected, corresponding to a minimum cluster size of 10.

In VBM analysis, in order to assess the evolution of regional GM atrophy during the entire AD course and at the early stage (a-MCI), voxelwise statistics was performed using the same model employed for the analysis of functional maps. Age, education and intra-cranial volume (ICV=GM volume+WM volume+CSF volume) were entered as covariates of no interest. This use of ICV as a covariate of no interest has been previously used in VBM studies [START_REF] Chételat | Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study[END_REF][START_REF] Serra | Grey and white matter changes at different stages of Alzheimer's disease[END_REF] in patients with AD and MCI. ICV is expected to correct for the potential bias due to inter-subject variability in head size without affecting the sensitivity of VBM to detect regional GM changes (effects of interest). Consistently with fMRI analyses, we first performed an F-contrast across groups (p=0.0005), thus obtaining the whole pattern of regional GM changes. Then, we investigated (within such a pattern) the structural changes which were present in patients with AD as compared to HS by performing a between groups T-contrast (post-hoc analysis). Our small sample size was insufficient to detect in a whole brain analysis GM changes at a corrected level between a-MCI patients and the other two groups (HS and AD). Thus, we decided to restrict the analysis to four strategic regions: the right and left hippocampus, the PCC and the mPFC. Coordinates to select these anatomical locations were taken from the maxima of the correspondent clusters obtained by an F-contrast across all groups (p=0.0005).

Spherical regions of interest (diameter=12mm) were then defined, and the following between groups T-contrasts were performed using a small volume correction: a-MCI vs. HS; a-MCI vs. AD (statistical threshold: p values family wise error corrected at voxel level <0.05). This small volume correction analysis has the advantage to be highly specific. Indeed, for the purposes of this study, it is critical to learn with a high confidence level whether specific brain regions of reduced connectivity are still structural preserved at the stage of a-MCI.

Results

Demographic characteristics. There were no differences between a-MCI and AD patients with respect to age, gender and education, while HS were younger and more educated than patients. As already mentioned, age and education were included in all neuroimaging analyses as covariates of no interest.

Neuropsychological evaluation at baseline. Groups performances obtained at all neuropsychological tests administered at baseline, are summarized in Table 2. The mean MMSE scores (corrected for education) were different between all groups (p<0.001, one-way ANOVA). A statistical group comparison showed that a-MCI patients performed worse than HS at all memory tests (p values<0.05). Moreover, they reported significantly lower scores than HS at the Phonological verbal fluency and Categorical fluency tests (p values<0.05), although their performance was within the range of normality, as defined above. Patients with AD compared to HS reported a widespread impairment of all the cognitive functions explored (p<0.05), including long and short term memory, executive functions, praxis abilities, language abilities and reasoning.

Clinical and neuropsychological follow-up At one year follow-up, 3 out of 10 a-MCI patients remained stable (30%) from a clinical and neuropsychological point of view, 3 out of 10 had developed a multi-domain MCI (30%), and 4 out of 10 had converted to dementia (40%).

Resting State fMRI. In Figure 2 (bottom panel) there are illustrated, for each z-map (respectively generated from PCC and mPFC), the main patterns of functional connectivity obtained in each studied group (HS, a-MCI and AD patients). Table 3 summarizes, for each analysis (z-maps respectively generated from PCC and mPFC), those brain regions presenting with significant changes in connectivity between groups.

When comparing patients with AD to HS, PCC showed a significant reduction of functional connectivity with the parahippocampal gyrus, the posterior and anterior cingulate, the superior frontal gyrus and the precuneus bilaterally. Reduced connectivity was also present within the left orbitofrontal cortex. In its turn, the mPFC, showed significantly reduced connectivity with the anterior cingulate and superior frontal gyrus bilaterally, and with the left orbitofrontal cortex and posterior cingulate.

A-MCI patients compared to HS showed a bilateral reduction of connectivity in the anterior cingulate and superior frontal gyrus, by considering either the PCC-or the mPFC-driven analysis. The mPFC-driven analysis revealed also an additional area of reduced connectivity in the posterior cingulate. The direct comparison between a-MCI and AD patients revealed a decrease of functional connectivity in the posterior cingulate cortex of the latter group in both analyses (PCC-and mPFC-driven), and in the parhaippocampal gyri in the PCC-driven analysis only.

VBM. None of the T1 weighted volumes of the participants were affected by macroscopic artefacts, as assessed by visual examination. As expected, AD patients compared to HS showed a widespread pattern of regional GM reduction, including the temporal and parietal lobes, the precuneus, the insular and the prefrontal cortex (See Table 4). Some of these regions relevant for the purposes of the study are also illustrated in Figure 3 (top panel, left side), together with plots showing mean (SD) values for each studied group (HS, a-MCI, AD) (bottom panel).

We also investigated the degree of regional GM atrophy in a-MCI patients as compared to both, HS and AD patients, using a small volume correction analysis. These results are summarized in Table 4. Patients with a-MCI compared to HS showed a significant reduction of regional GM volumes in the hippocampus and mPFC bilaterally (p corrected <0.05), but not in the PCC/precuneus. In contrast, they showed increased GM volumes in the PCC/precuneus (p corrected <0.05), but not in the hippocampi and mPFC when compared to patients with AD.

Discussion

The current study combines functional and structural MRI to quantify the relative role played by regional GM atrophy over the transitional stage between normal aging and dementia, by considering a-MCI as an early clinical stage of AD. Although this last assumption was not fully verified in all recruited patients with a-MCI, the clinicalneuropsychological follow-up at 1 year demonstrated a conversion to AD in 40% of them, and accumulation of additional impairments in cognitive domains other than memory in another 30 %. This indicates that our a-MCI group may be regarded, at least in a relevant part, as including patients "in a prodromal stage of AD", as defined by some authoritative authors [START_REF] Petersen | Conceptual overview[END_REF] . Specific patterns of structural and functional changes were identified within the DMN, with a different expression associated with an early (a-MCI) and with an advanced stage of the disease (fully developed dementia). Our functional investigation was focused on assessing changes of brain connectivity as a function of AD evolution between two regions of the DMN, which were automatically identified using ICA, and the rest of the brain. As for functional data analysis, regional patterns of GM atrophy were also detected in a data driven fashion, using VBM.

VBM analysis returned patterns of GM atrophy which are consistent with previous literature, with a widespread involvement of association cortices in demented patients 4 15 , and a more confined subset of atrophic regions in patients with a-MCI [START_REF] Bozzali | The contribution of voxel-based morphometry in staging patients with mild cognitive impairment[END_REF] . For the purposes of this study, we mainly focussed on those brain regions in which GM atrophy and functional disconnection showed anatomical overlap in patients with fully developed dementia as compared to HS. Then, we investigated functional and structural changes across AD evolution, by comparing a-MCI with AD patients and HS. The PCC revealed the presence of functional disconnection with the anterior cingulate and superior frontal gyrus as a feature of the a-MCI stage, even in the absence of a (detectable) local GM loss within the PCC/precuneus. In this latter region, we were unable to find significant GM changes between a-MCI patients and HS even restricting the analysis to a small volume of interest (Table 4). Consistently, but in opposite direction, PFC showed a reduced connectivity with PCC in patients with a-MCI. These findings are consistent with a large body of literature that, using PET, has consistently demonstrated a reduction of metabolism in the PCC of patients with early AD 3 5-10 , in the absence of a remarkable atrophy 9 10 . It has been suggested by many authors that this posterior hypometabolism is likely to express a reduction of neuronal afferents from medial temporal lobes to PCC, mainly due to hippocampal atrophy [START_REF] Buckner | Memory and executive function in aging and AD: multiple factors that cause decline and reserve factors that compensate[END_REF] . According to our data, the local atrophy of the medial temporal lobes is equally present in both patients groups, AD and a-MCI (Figure 3, plot 3). As expected, there is a pattern of functional disconnection between the parahippocampi and regions of the DMN which is already remarkable at the stage of a-MCI. However, it is only in AD patients that this correlation is completely lost, as suggested by its negative trend (Figure 3, plot 6). All these data are consistent with a recent study that provided a direct evidence supporting disconnection as a major factor that contributes to the posterior hypometabolism in patients with early AD [START_REF] Villain | Relationships between hippocampal atrophy, white matter disruption, and gray matter hypometabolism in Alzheimer's disease[END_REF] . In this perspective, our results help to further elucidate the differential role of disconnection and GM atrophy in the PCC, the latter occurring only at more advanced stages of the disease. As further discussed below, this evolution of brain tissue abnormalities in the PCC from functional to structural changes might be critical for the clinical conversion from a-MCI to AD. The pattern of GM atrophy we observed in a-MCI patients involves also the medial prefrontal cortex bilaterally.

Hippocampus and medial prefrontal cortex are regions structurally connected to each other through the cingulate bundle, and they are both involved, at different level, in memory processing 39 40 , the only function that was frankly impaired in our cohort of a-MCI patients.

We might speculate that the reduced connectivity found in the PCC of a-MCI patients is not only associated with hippocampal atrophy, but also with the GM loss observed in frontal regions, which are also connected to the PCC by the cingulate bundle. This suggests that the hippocampus is not the only structure responsible for PCC abnormalities in AD evolution. PCC is believed to be one of the key nodes of the so called DMN, whose disruption has been demonstrated not only to be present in AD patients [START_REF] Greicius | Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI[END_REF] , but also to correlate with global measures of cognitive decline [START_REF] Sorg | Selective changes of resting-state networks in individuals at risk for Alzheimer's disease[END_REF] . We further argue that the late atrophy of the PCC/precuneus, which follows an early reduction of functional connectivity, may be crucial for the clinical conversion from a-MCI to multidomain MCI, and finally to fully developed dementia. In this perspective, the PCC would represent not only an early diagnostic marker of AD [START_REF] Greicius | Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI[END_REF] , but also a potential marker of conversion from MCI to AD [START_REF] Wang | Altered functional connectivity in early Alzheimer's disease: a resting-state fMRI study[END_REF] .

The approach used in the current study seems to be particularly promising in clarifying the patho-physiology of AD evolution. However, it should be noted that voxels compared in VBM versus those compared in resting state fMRI analysis are in the same anatomical regions but overlap only partially, and that different statistical thresholds were used for the two datasets. Therefore, a direct comparison of structural and functional data should be viewed with caution. The method of analysis could indeed be improved by developing a specific technique for cross-modality imaging comparisons (in this case, functional and structural).

The main limitations lie in the small sample size, and in age and education differences between patients and controls. We tried to minimize the impact of small sample size by age and education as covariates of no interest in all imaging analyses. Nevertheless, further studies are needed to confirm our results, and to extend the investigation to larger populations, possibly including subgroups of patients at different level of cognitive impairment. Finally, we gave a longitudinal interpretation of our results (i.e. decreased connectivity in PCC precedes GM atrophy in AD evolution) on the basis of a theoretical model which considers AD as a continuum of cognitive decline between normal aging and dementia, with a-MCI as a prodromal stage of the disease. However, we should reiterate that the current study has employed a cross-sectional design, and our findings need be confirmed by further longitudinally-based studies.

Figure legends

Figure 1. The figure shows the processing pipeline of resting state functional connectivity analysis. Group Independent Component Analysis (ICA) was first used to concatenate individual fMRI data across time, thus producing, for each subject, a computation of components and time courses (back reconstruction). From the functionally relevant components, we identified, subject by subject, the default mode network (DMN), which is characterized by the synchronous oscillation of the posterior cingulate cortex (PCC), the lateral parietal and medial prefrontal cortex (mPFC). The two regions showing the highest peak z-score in the mean spatial map, namely the PCC and the mPFC, were chosen as regions-of-interest (ROIs) to produce, subject by subject, maps for the correlation analysis. In each subject, these two ROIs were defined by limiting each of them to those 100 voxels which reported the highest score in the ICAdriven DMN. Finally, each ROI was used to produce correlation maps between voxels in the PCC and mPFC and all the remaining voxels of the brain, thus producing, for each subject, the correspondent PCC-and mPFC -driven correlation maps. Each correlation map was then converted to normal z values using Fisher's r-to-z transformation, and used for statistical group analyses. For some relevant regions, the correspondent mean (SD) values are plotted for the three studied groups (bottom panel).

See text for further details. For each administered test appropriate adjustments for sex, age and education were applied according to the Italian normative data.

Cut-off scores of normality (≥95% of the lower tolerance limit of the normal population distribution) are also reported. B=bilateral.

There are here reported regional changes in functional connectivity between all groups (a-MCI, AD, and HS). Between group comparisons (T-contrasts) were performed as post hoc analyses by masking them with the global effect estimated across all groups by F-contrast (p=0.001). Statistical threshold for Tcontrasts was set to p values <0.001 at uncorrected level. Each region size is expressed in number of voxels. Only regions with a minimum size of 10 voxels have been considered as statistically significant.

In all the areas where we found between-group differences, there was a positive correlation between the two regions in healthy controls. The reductions found in patients correspond to a reduction in the strength of correlation. The only two areas were there was an inversion in the sign of the correlation (from positive to negative) were the L and R parahippocampal gyri, in patients with AD.

See text for further details. There are here reported significant changes of regional grey matter (GM) volume between groups. The comparison between AD patients and HS was performed including the whole brain. The T-contrast was performed as a post hoc analysis (statistical threshold cluster level corrected < 0.001) by masking it with the global effect across all groups estimated by F-contrast (p=0.0005). The comparison between a-MCI patients and HS and AD patients, was performed using the small volume correction in four anatomical locations which are critical for the combination of functional and structural investigations.

Coordinates for these anatomical locations were taken from the maxima resulting from the F-contrast across all groups (p=0.0005). Spherical regions of interest (diameter=12mm) were then defined, and the following between groups T-contrasts were performed: a-MCI vs. HS; a-MCI vs. AD (statistical threshold: p values family wise error corrected at voxel level <0.05). Each region size is expressed in number of voxels. See text for further details.

; 4 )

 4 Problem-solving: Raven's Coloured Progressive Matrices 20 ; 5) Executive functions and attention: Phonological Word Fluency 20 , Categorical Word Fluency 21 , Trail Making Test 25 and the Attentive Matrices 21 ; 6) Praxis: Copy of drawings 21 , Copy of Complex Rey's Figure 22 .

Figure 3 (

 3 Figure 3 (top panel, right side) illustrates the patterns of functional changes, respectively

Figure 2 .

 2 Figure 2. Top panel illustrates the mean spatial independent component (IC) pattern

Figure 3 .

 3 Figure 3. Distribution of reduced regional GM volume (top panel on the left) and

Table 1 . Principal demographic and clinical characteristics of studied subjects.

 1

	Gili et al. 24

Table 2 Neuropsychological assessment of studied subjects.

 2

	Cognitive function	Neuropsychological Test (cut-off score)		Mean (SD) scores
			a-MCI	AD	HS
	Global measure
		Mini Mental State Examination (≥23.8)	25.1 (1.2)#	19.7 (4.5)* §	28.34 (2.0)
	Long term memory
	Verbal episodic	15 Rey's words list (Immediate recall) (>28.5)	32.0 (5.4)#	20.4 (9.2)* §	45.2 (9.4)
		15 Rey's words list (Delayed recall) (>4.7)	4.7 (2.6)#	0.5 (1.2)* §	10.0 (2.3)
		Story recall (≥2.39)	4.9 (3.8)#	2.5 (2.9)* §	11.1 (1.9)
	Visuo-spatial episodic	Rey's Complex Figure (Delayed recall) (>6.3)	8.8 (5.0)#	0.3 (1.0)* §	15.0 (8.2)
	Short term memory
		Digit span (>3.7)	5.2 (0.8)	4.7 (1.1)*	6.0 (1.2)
		Corsi Block tapping task (>3.5)	4.2 (1)#	3.4 (1.5)*	5.4 (0.6)
	Language

Table 3 . Regional differences in functional connectivity between patients with a-MCI, AD, and HS.

 3

	Side	Size	Coordinates (mm) Peak Z-score
			x	y	z
	AD patients < HS

-MCI patients < HS PCC-driven functional connectivity

	Posterior cingulate cortex	B	21	-4	-52	22	3.22
	Parahippocampal gyrus	R	29	26	0	-24	3.21
	Parahippocampal gyrus	L	10	-22	-16	-24	3.18
	mPFC-driven functional connectivity
	Posterior cingulate cortex	B	11	-8	-24	3	3.30
	Abbreviations: AD= Alzheimer's disease; a-MCI= amnesic mild cognitive impairment; HS=healthy
	subjects; PCC= posterior cingulated cortex; mPFC= medial prefrontal cortex; L=left; R=right;
	Anterior cingulate/superior frontal gyrus	B	122	2	46	14	3.60
	mPFC-driven functional connectivity
	Anterior cingulate/superior frontal gyrus	B	17	2	46	30	3.18
	Posterior cingulate cortex	B	16	4	-30	36	3.13
	a-MCI > AD patients
	PCC-driven functional connectivity

Table 4 . Regional grey matter atrophy between patients with a-MCI, AD, and HS.

 4

	Brain region	Side Size Coordinates (mm) Peak Z-score
				x	y	z
	AD patients < HS
				Whole brain analysis
	Posterior cingulated cortex/Precuneous	B	1797	-6	-66	52	5.62
	Inferior temporal gyrus	R	445	-50	-62	-18	5.38
	Fusiform gyrus	R		-30	-86	-26	5.12
	Orbitofrontal cortex	R	286	46	32	-14	5.03
	Hippocampus	R	107	10	-2	-16	4.75
	Hippocampus	L	78	-4	-4	-14	4.73
	Angular gyrus	L	469	-64	-42	24	4.71
	Medial frontal and orbital cortex	B	306	-4	54	-12	4.53
	Orbitofrontal cortex	L	11	-44	30	-18	4.40
	Insula	R	231	32	3	8	4.32
	Insula	L	72	-30	6	6	4.33
	a-MCI patients < HS
				Small volume analysis
	Hippocampus	R	455	18	0	-16	3.16
	Hippocampus	L	245	-8	0	-16	3.50
	Posterior cingulated cortex/Precuneous	B	-	-6	-64	52	N.S.C.
	Medial prefrontal and orbital cortex	B	575	-2	50	-12	3.71
	a-MCI > AD patients
				Small volume analysis

Acknowledgments

The authors thank Dr. Emiliano Macaluso for his help with data analysis.

Founding

This project has been supported by grants of the Italian Ministry of Health and of the Italian Ministry of University and Research.

Competing interests

The authors declare that they have no actual or potential conflicts of interest.

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