Iolanda Martino

Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results.

Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

Near-Infrared Spectroscopy in Gait Disorders: Is It Time to Begin?

Walking is a complex motor behavior with a special relevance in clinical neurology. Many neurological diseases, such as Parkinson’s disease and stroke, are characterized by gait disorders whose neurofunctional correlates are poorly investigated. Indeed, the analysis of real walking with the standard neuroimaging techniques poses strong challenges, and only a few studies on motor imagery or walking observation have been performed so far. Functional near-infrared spectroscopy (fNIRS) is becoming an important research tool to assess functional activity in neurological populations or for special tasks, such as walking, because it allows investigating brain hemodynamic activity in an ecological setting, without strong immobility constraints. A systematic review following PRISMA guidelines was conducted on the fNIRS-based examination of gait disorders. Twelve of the initial yield of 489 articles have been included in this review. The lesson learnt from these studies suggest that oxy-hemoglobin levels within the prefrontal and premotor cortices are more sensitive to compensation strategies reflecting postural control and restoration of gait disorders. Although this field of study is in its relative infancy, the evidence provided encourages the translation of fNIRS in clinical practice, as it offers a unique opportunity to explore in depth the activity of the cortical motor system during real walking in neurological patients. We also discuss to what extent fNIRS may be applied for assessing the effectiveness of rehabilitation programs.

Psychopathological constellation in patients with PNES: A new hypothesis

Depression symptoms have often reported in patients with psychogenic nonepileptic seizures (PNES), although the underlying psychopathological symptomatology has been poorly understood. Our aim was to compare constellations of psychological and behavioral disturbance in PNES with respect to patients with mild-major depressive disorder (MDD), hypothesizing that the construct of depression might be different in the two groups. Ten patients with PNES and ten sex-/age-matched patients with mild-MDD newly-diagnosed, were enrolled in this study. A wide neuropsychiatric battery was employed including the following: symptoms checklist 90-R (SCL-90-R), Toronto alexithymia scale (TAS-20), Hamilton anxiety rating scale (HAMA), Beck depression inventory (BDI II), dissociative experiences scale (DES), traumatic experience checklist (TEC), somatoform dissociation questionnaire (SDQ-20), and temperament and character inventory-revised (TCI-R). No significant difference was detected in the large part of psychopathological examination including personality profile between the two groups. However, PNES showed high scores in alexithymia (p=0.02); anxiety (p=0.03), and somatoform symptomatology (ps<0.03) with respect to patients with mild-MDD. Moreover, somatoform symptoms strongly correlated with depression scores in both groups, whereas alexithymia was influenced by high anxiety level only in the group with PNES. No significant relationship was found between traumatic experience (as measured by TEC) and construct of depression. Our proof-of-concept study suggests that patients with PNES are characterized by their inability to verbalize emotions when dealing with anxiety symptoms, therefore expressing them in a somatic dimension. Further researches, including the investigation of the relationship between anxiety status and emotional expression, are warranted to better understand the pathogenesis of PNES.

Increased cerebellar gray matter volume in head chefs

Objective Chefs exert expert motor and cognitive performances on a daily basis. Neuroimaging has clearly shown that that long-term skill learning (i.e., athletes, musicians, chess player or sommeliers) induces plastic changes in the brain thus enabling tasks to be performed faster and more accurately. How a chefs expertise is embodied in a specific neural network has never been investigated. Methods Eleven Italian head chefs with long-term brigade management expertise and 11 demographically-/ psychologically- matched non-experts underwent morphological evaluations. Results Voxel-based analysis performed with SUIT, as well as, automated volumetric measurement assessed with Freesurfer, revealed increased gray matter volume in the cerebellum in chefs compared to non-experts. The most significant changes were detected in the anterior vermis and the posterior cerebellar lobule. The magnitude of the brigade staff and the higher performance in the Tower of London test correlated with these specific gray matter increases, respectively. Conclusions We found that chefs are characterized by an anatomical variability involving the cerebellum. This confirms the role of this region in the development of similar expert brains characterized by learning dexterous skills, such as pianists, rock climbers and basketball players. However, the nature of the cellular events underlying the detected morphological differences remains an open question.

The mystery of unexplained traumatic sudden falls. A clinical case that adds a new feasible cause

In clinical neurology practice, sudden falls with loss of consciousness are considered organic diseases easy to diagnose, but sometimes, the mystery of the etiology remains unsolved [1]. Many diseases encounter falls, such as epilepsy, vascular problems, sleep disorders, paroxysmal movement disorders, narcolepsy, cardiac syncope, etc, however, to get a certain diagnosis requires long time and many investigations [1]. A 45-year-old woman was admitted at our university hospital because of a three years history of unsolved refractory epilepsy [2] after being treated with several drugs at maximum dosages, such as carbamazepine, levetiracetam, and lamotrigine. Her past medical record was unremarkable, but she had a 3-year history of stereotyped episodes lasting few seconds characterised by a brief staring followed by sudden drop attacks with apparent loss of consciousness causing serious injuries and traumas in some of them (she was burned while cooking and she reported several deep cuts that required the use of sutures) occurring several times a week. Before epilepsy, cardiac syncope was suspected, thus although electrocardiogram (ECG), intensive ECG registration, blood pressure monitoring, transthoracic echocardiogram, supra aortic trunks ultrasonography, and head-up tilt test were normal, a subcutaneous loop recorder was implanted to exclude arrhythmia. Moreover, surprisingly, many episodes as reported in the history were captured without critical cardiac events. At our Neurology Unit, she underwent extensive electroclinical and imaging investigation. Neurological examination, serum, and liquor (including hypocretin dosage) were normal. She underwent several awake and asleep electroencephalograms and 3T brain imaging that resulted all normal. Intriguingly, during an intensive video-EEG recording, three emblematic episodes, as reported in anamnesis, were recorded without any electrical correlation. Thus, a psychogenic nonepileptic seizure (PNES) was diagnosed and a video-EEG record with induction further confirmed the diagnosis. As summarized in Table 1, after diagnosis of PNES, psychiatric assessment clearly demonstrated a conversion disorder (DSM-5 300.11 F44.6) [3] but does not include all the criteria for a specific personality disorder diagnosis. Indeed, a neuropsychological battery showed visuospatial and visuoconstructive impairment associated with selective attention deficit. She was then started with venlafaxine 150 mg daily, perphenazine 2 mg daily, and lamotrigine 75 mg daily together with a cognitive behavioral therapy (CBT). Three months later, the episodes with loss of consciousness almost disappeared and there was a partial insight for the mood disorder. Beck Depression Inventory and Hamilton Anxiety Rating Scale were reduced of 5 and 6 points, respectively, while the Insight and Treatment Attitudes Questionnaire scores increased up to 16. The observation of ‘‘incongruency’’ between the clinical features evoking epileptic fits with loss of consciousness and the normality of both EEG/ECG recordings was our starting point to describe the current case. A. Bruni and L. Martino contributed equally to this work.

The application of artificial intelligence to understand the pathophysiological basis of psychogenic nonepileptic seizures

Psychogenic nonepileptic seizures (PNES) are episodes of paroxysmal impairment associated with a range of motor, sensory, and mental manifestations, which perfectly mimic epileptic seizures. Several patterns of neural abnormalities have been described without identifying a definite neurobiological substrate. In this multicenter cross-sectional study, we applied a multivariate classification algorithm on morphological brain imaging metrics to extract reliable biomarkers useful to distinguish patients from controls at an individual level. Twenty-three patients with PNES and 21 demographically matched healthy controls (HC) underwent an extensive neuropsychiatric/neuropsychological and neuroimaging assessment. One hundred and fifty morphological brain metrics were used for training a random forest (RF) machine-learning (ML) algorithm. A typical complex psychopathological construct was observed in PNES. Similarly, univariate neuroimaging analysis revealed widespread neuroanatomical changes affecting patients with PNES. Machine-learning approach, after feature selection, was able to perform an individual classification of PNES from controls with a mean accuracy of 74.5%, revealing that brain regions influencing classification accuracy were mainly localized within the limbic (posterior cingulate and insula) and motor inhibition systems (the right inferior frontal cortex (IFC)). This study provides Class II evidence that the considerable clinical and neurobiological heterogeneity observed in individuals with PNES might be overcome by ML algorithms trained on surface-based magnetic resonance imaging (MRI) data.

Hypersomnia hiding a bipolar disorder

To the Editor: The Diagnostic and Statistical Manual of Mental Disorders-Five Edition (DSM-5) [1] defines hypersomnolence as: Ba broad diagnostic term and includes symptoms of excessive quantity of sleep, deteriorated quality of wakefulness, and sleep inertia (Criterion A).^ Furthermore, hypersomnia has been often clinically identified as a symptom in young patients with bipolar spectrum mainly with depression traits but never confirmed by instruments (Steinan et al., 2016). Moreover, during hypersomnia nocturnal polysomnography (PSG) demonstrates a normal to prolonged sleep duration, short sleep latency, and normal to increased sleep continuity. The distribution of rapid eye movement (REM) sleep is also normal. Sleep efficiency is mostly greater than 90%. Multiple sleep latency test (MSLT) is the gold standard for differential diagnosis in patients with hypersomnia and narcolepsy. Curiously, in the psychiatric assessment, the diagnosis of hypersomnolence is mainly made by taking the patient history except for only five subjects out of 25 patients with BP reported in the current literature [2]. The present case further highlights that a precise diagnosis and a comprehensive approach to the hypersomnolent patient in order to include psychiatric disorders into the differential diagnosis (e.g., to exclude psychiatric comorbidity for a diagnosis of any central nervous system hypersomnia) is mandatory. A 48-year-old woman was admitted at our Neurology Unit because of long-lasting history of hypersomnolence. The patient reported, during the last 4 months, abnormally long night sleep, difficult awakening in the morning, confusion, and disorientation. These episodes lasted between six daily hours till more than 2 days. General and neurological examination was normal. The patient did not report symptoms such as sleep paralyses/hallucinations or snoring. The patient referred a prolonged night sleep without interruption and night terror occurrence, particularly difficulty in awakening and persistent daytime sleepiness. She never complained about sudden loss of muscle tone such as cataplectic episodes neither hallucination. The Epworth Sleepiness Questionnaire showed a score of 16. Thus, from the clinical point of view, the diagnosis of narcolepsy with or without cataplexy was excluded according to the DSM-5 Criteria (347.01 DSM-5). She underwent extensive electro-clinical, neuropsychiatric, and imaging investigation. Routine and awake electroencephalograms were normal as well as 3T brain imaging. A PSG demonstrates normal sleep, except for its prolonged duration. Sleep apnea or periodic limb movement were not recorded. MSLT showed a mean sleep latency of 6.11 min (normal range 10–20 min) and two episodes of sleep-onset REM (SOREM) periods in Iolanda Martino and Antonella Bruni contributed equally to this work