Simple 1-D Convolutional Networks for Resting-State fMRI Based Classification in Autism

Deep learning methods are increasingly being used with neuroimaging data like structural and function magnetic resonance imaging (MRI) to predict the diagnosis of neuropsychiatric and neurological disorders. For psychiatric disorders in particular, it is believed that one of the most promising modality is the resting-state functional MRI (rsfMRI), which captures the intrinsic connectivity between regions in the brain. Because rsfMRI data points are inherently high-dimensional (~1M), it is impossible to process the entire input in its raw form. In this paper, we propose a very simple transformation of the rsfMRI images that captures all of the temporal dynamics of the signal but sub-samples its spatial extent. As a result, we use a very simple 1-D convolutional network which is fast to train, requires minimal preprocessing and performs at par with the state-of-the-art on the classification of Autism spectrum disorders.Comment: accepted for publication in IJCNN 201

The two decades brainclinics research archive for insights in neurophysiology (TDBRAIN) database

In neuroscience, electroencephalography (EEG) data is often used to extract features (biomarkers) to identify neurological or psychiatric dysfunction or to predict treatment response. At the same time neuroscience is becoming more data-driven, made possible by computational advances. In support of biomarker development and methodologies such as training Artificial Intelligent (AI) networks we present the extensive Two Decades-Brainclinics Research Archive for Insights in Neurophysiology (TDBRAIN) EEG database. This clinical lifespan database (5–89 years) contains resting-state, raw EEG-data complemented with relevant clinical and demographic data of a heterogenous collection of 1274 psychiatric patients collected between 2001 to 2021. Main indications included are Major Depressive Disorder (MDD; N = 426), attention deficit hyperactivity disorder (ADHD; N = 271), Subjective Memory Complaints (SMC: N = 119) and obsessive-compulsive disorder (OCD; N = 75). Demographic-, personality- and day of measurement data are included in the database. Thirty percent of clinical and treatment outcome data will remain blinded for prospective validation and replication purposes. The TDBRAIN database and code are available on the Brainclinics Foundation website at www.brainclinics.com/resources and on Synapse at www.synapse.org/TDBRAIN

The role of habit in compulsivity.

Compulsivity has been recently characterized as a manifestation of an imbalance between the brain׳s goal-directed and habit-learning systems. Habits are perhaps the most fundamental building block of animal learning, and it is therefore unsurprising that there are multiple ways in which the development and execution of habits can be promoted/discouraged. Delineating these neurocognitive routes may be critical to understanding if and how habits contribute to the many faces of compulsivity observed across a range of psychiatric disorders. In this review, we distinguish the contribution of excessive stimulus-response habit learning from that of deficient goal-directed control over action and response inhibition, and discuss the role of stress and anxiety as likely contributors to the transition from goal-directed action to habit. To this end, behavioural, pharmacological, neurobiological and clinical evidence are synthesised and a hypothesis is formulated to capture how habits fit into a model of compulsivity as a trans-diagnostic psychiatric trait.CM Gillan is supported by a Sir Henry Wellcome Postdoctoral Fellowship (101521/Z/12/Z).This is the final version of the article. It was first available from Elsevier via https://doi.org/10.1016/j.euroneuro.2015.12.03

Brain circuitry of compulsivity.

Compulsivity is associated with alterations in the structure and the function of parallel and interacting brain circuits involved in emotional processing (involving both the reward and the fear circuits), cognitive control, and motor functioning. These brain circuits develop during the pre-natal period and early childhood under strong genetic and environmental influences. In this review we bring together literature on cognitive, emotional, and behavioral processes in compulsivity, based mainly on studies in patients with obsessive-compulsive disorder and addiction. Disease symptoms normally change over time. Goal-directed behaviors, in response to reward or anxiety, often become more habitual over time. During the course of compulsive disorders the mental processes and repetitive behaviors themselves contribute to the neuroplastic changes in the involved circuits, mainly in case of chronicity. On the other hand, successful treatment is able to normalize altered circuit functioning or to induce compensatory mechanisms. We conclude that insight in the neurobiological characteristics of the individual symptom profile and disease course, including the potential targets for neuroplasticity is an unmet need to advance the field.Dr. Soriano-Mas is funded by a ׳Miguel Servet׳ contract from the Carlos III Health Institute (CP10/00604). Dr. Goudriaan is supported by a VIDI Innovative Research Grant (Grant no. 91713354) funded by the Dutch Scientific Research Association (NWO-ZonMW). Dr. Alonso was funded by the Instituto de Salut Carlos III-FISPI14/00413. Dr. Nakamae received Grant support from MEXT KAKENHI (Nos. 24791223 and 26461753).This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.euroneuro.2015.12.00

Predicting the naturalistic course in anxiety disorders using clinical and biological markers:a machine learning approach

BackgroundDisease trajectories of patients with anxiety disorders are highly diverse and approximately 60% remain chronically ill. The ability to predict disease course in individual patients would enable personalized management of these patients. This study aimed to predict recovery from anxiety disorders within 2 years applying a machine learning approach.MethodsIn total, 887 patients with anxiety disorders (panic disorder, generalized anxiety disorder, agoraphobia, or social phobia) were selected from a naturalistic cohort study. A wide array of baseline predictors (N = 569) from five domains (clinical, psychological, sociodemographic, biological, lifestyle) were used to predict recovery from anxiety disorders and recovery from all common mental disorders (CMDs: anxiety disorders, major depressive disorder, dysthymia, or alcohol dependency) at 2-year follow-up using random forest classifiers (RFCs).ResultsAt follow-up, 484 patients (54.6%) had recovered from anxiety disorders. RFCs achieved a cross-validated area-under-the-receiving-operator-characteristic-curve (AUC) of 0.67 when using the combination of all predictor domains (sensitivity: 62.0%, specificity 62.8%) for predicting recovery from anxiety disorders. Classification of recovery from CMDs yielded an AUC of 0.70 (sensitivity: 64.6%, specificity: 62.3%) when using all domains. In both cases, the clinical domain alone provided comparable performances. Feature analysis showed that prediction of recovery from anxiety disorders was primarily driven by anxiety features, whereas recovery from CMDs was primarily driven by depression features.ConclusionsThe current study showed moderate performance in predicting recovery from anxiety disorders over a 2-year follow-up for individual patients and indicates that anxiety features are most indicative for anxiety improvement and depression features for improvement in general

Negative cognitive schema modification as mediator of symptom improvement after electroconvulsive therapy in major depressive disorder

Background: Electroconvulsive therapy (ECT) is a potent option for treatment-resistant major depressive disorder (MDD). Cognitive models of depression posit that negative cognitions and underlying all-or-nothing negative schemas contribute to and perpetuate depressed mood. This study investigates whether ECT can modify negative schemas, potentially via memory reactivation, and whether such changes are related to MDD symptom improvement. Method: Seventy-two patients were randomized to either an emotional memory reactivation electroconvulsive therapy (EMR-ECT) or control memory reactivation electroconvulsive therapy (CMR-ECT) intervention prior to ECT-sessions in a randomized controlled trail. Emotional memories associated with patients' depression were reactivated before ECT-sessions. At baseline and after the ECT-course, negative schemas and depression severity were assessed using the Dysfunctional Attitude Scale (DAS) and Hamilton Depression Rating Scale HDRS. Mediation analyses were used to examine whether the effects of ECT on HDRS-scores were mediated by changes in DAS-scores or vice versa. Results: Post-ECT DAS-scores were significantly lower compared to baseline. Post-ECT, the mean HDRS-score of the whole sample (15.10 ± 8.65 [SD]; n = 59) was lower compared to baseline (24.83 ± 5.91 [SD]). Multiple regression analysis showed no significant influence of memory reactivation on schema improvement. Path analysis showed that depression improvement was mediated by improvement of negative cognitive schemas. Conclusion: ECT is associated with improvement of negative schemas, which appears to mediate the improvement of depressive symptoms. An emotional memory intervention aimed to modify negative schemas showed no additional effect

Reduced frontal brain volume in non-treatment-seeking cocaine-dependent individuals:Exploring the role of impulsivity, depression, and smoking

In cocaine-dependent patients, gray matter (GM) volume reductions have been observed in the frontal lobes that are associated with the duration of cocaine use. Studies are mostly restricted to treatment-seekers and studies in non-treatment-seeking cocaine abusers are sparse. Here, we assessed GM volume differences between 30 non-treatment-seeking cocaine-dependent individuals and 33 non-drug using controls using voxel-based morphometry. Additionally, within the group of non-treatment-seeking cocaine-dependent individuals, we explored the role of frequently co-occurring features such as trait impulsivity (Barratt Impulsivity Scale, BIS), smoking, and depressive symptoms (Beck Depression Inventory), as well as the role of cocaine use duration, on frontal GM volume. Smaller GM volumes in non-treatment-seeking cocaine-dependent individuals were observed in the left middle frontal gyrus. Moreover, within the group of cocaine users, trait impulsivity was associated with reduced GM volume in the right orbitofrontal cortex, the left precentral gyrus, and the right superior frontal gyrus, whereas no effect of smoking severity, depressive symptoms, or duration of cocaine use was observed on regional GM volumes. Our data show an important association between trait impulsivity and frontal GM volumes in cocaine-dependent individuals. In contrast to previous studies with treatment-seeking cocaine-dependent patients, no significant effects of smoking severity, depressive symptoms, or duration of cocaine use on frontal GM volume were observed. Reduced frontal GM volumes in non-treatment-seeking cocaine-dependent subjects are associated with trait impulsivity and are not associated with co-occurring nicotine dependence or depression

Changes in functioning of mesolimbic incentive processing circuits during the premenstrual phase

The premenstrual phase of the menstrual cycle is associated with marked changes in normal and abnormal motivated behaviors. Animal studies suggest that such effects may result from actions of gonadal hormones on the mesolimbic dopamine (DA) system. We therefore investigated premenstrual changes in reward-related neural activity in terminal regions of the DA system in humans. Twenty-eight healthy young women underwent functional magnetic resonance imaging on 2 days during the menstrual cycle, once during the late follicular phase and once during the premenstrual phase, in counterbalanced order. Using a modified version of the monetary incentive delay task, we assessed responsiveness of the ventral striatum to reward anticipation. Our results show enhanced ventral striatal responses during the premenstrual as compared to the follicular phase. Moreover, this effect was most pronounced in women reporting more premenstrual symptoms. These findings provide support for the notion that changes in functioning of mesolimbic incentive processing circuits may underlie premenstrual changes in motivated behaviors. Notably, increases in reward-cue responsiveness have previously been associated with DA withdrawal states. Our findings therefore suggest that the sharp decline of gonadal hormone levels in the premenstrual phase may trigger a similar withdrawal-like state