From Connectivity Models to Region Labels: Identifying Foci of a Neurological Disorder

We propose a novel approach to identify the foci of a neurological disorder based on anatomical and functional connectivity information. Specifically, we formulate a generative model that characterizes the network of abnormal functional connectivity emanating from the affected foci. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. We employ the variational expectation-maximization algorithm to fit the model and subsequently identify both the afflicted regions and the differences in connectivity induced by the disorder. We demonstrate our method on a population study of schizophrenia.National Alliance for Medical Image Computing (U.S.) (Grant NIH NIBIB NAMIC U54-EB005149)Neuroimaging Analysis Center (U.S.) (Grant NIH NCRR NAC P41-RR13218)Neuroimaging Analysis Center (U.S.) (Grant NIH NCRR NAC P41-EB015902)National Science Foundation (U.S.) (CAREER Grant 0642971)National Institutes of Health (U.S.) (R01MH074794)National Institutes of Health (U.S.). Advanced Multimodal Neuroimaging Training Progra

EMDR therapy for PTSD after motor vehicle accidents: meta-analytic evidence for specific treatment

Motor vehicle accident (MVA) victims may suffer both acute and post-traumatic stress disorders (PTSD). With PTSD affecting social, interpersonal and occupational functioning, clinicians as well as the National Institute of Health are very interested in identifying the most effective psychological treatment to reduce PTSD. From research findings, eye movement desensitization and reprocessing (EMDR) therapy is considered as one of the effective treatment of PTSD. In this paper, we present the results of a meta-analysis of fMRI studies on PTSD after MVA through activation likelihood estimation. We found that PTSD following MVA is characterized by neural modifications in the anterior cingulate cortex (ACC), a cerebral structure involved in fear-conditioning mechanisms. Basing on previous findings in both humans and animals, which demonstrate that desensitization techniques and extinction protocols act on the limbic system, the effectiveness of EMDR and of cognitive behavioral therapies (CBT) may be related to the fact that during these therapies the ACC is stimulated by desensitization

Generative models of brain connectivity for population studies

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 131-139).Connectivity analysis focuses on the interaction between brain regions. Such relationships inform us about patterns of neural communication and may enhance our understanding of neurological disorders. This thesis proposes a generative framework that uses anatomical and functional connectivity information to find impairments within a clinical population. Anatomical connectivity is measured via Diffusion Weighted Imaging (DWI), and functional connectivity is assessed using resting-state functional Magnetic Resonance Imaging (fMRI). We first develop a probabilistic model to merge information from DWI tractography and resting-state fMRI correlations. Our formulation captures the interaction between hidden templates of anatomical and functional connectivity within the brain. We also present an intuitive extension to population studies and demonstrate that our model learns predictive differences between a control and a schizophrenia population. Furthermore, combining the two modalities yields better results than considering each one in isolation. Although our joint model identifies widespread connectivity patterns influenced by a neurological disorder, the results are difficult to interpret and integrate with our regioncentric knowledge of the brain. To alleviate this problem, we present a novel approach to identify regions associated with the disorder based on connectivity information. Specifically, we assume that impairments of the disorder localize to a small subset of brain regions, which we call disease foci, and affect neural communication to/from these regions. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. Once again, we use a probabilistic formulation: latent variables specify a template organization of the brain, which we indirectly observe through resting-state fMRI correlations and DWI tractography. Our inference algorithm simultaneously identifies both the afflicted regions and the network of aberrant functional connectivity. Finally, we extend the region-based model to include multiple collections of foci, which we call disease clusters. Preliminary results suggest that as the number of clusters increases, the refined model explains progressively more of the functional differences between the populations.by Archana Venkataraman.Ph.D

A Hidden Markov Factor Analysis Framework for Seizure Detection in Epilepsy Patients

Approximately 1% of the world population suffers from epilepsy. Continuous long-term electroencephalographic (EEG) monitoring is the gold-standard for recording epileptic seizures and assisting in the diagnosis and treatment of patients with epilepsy. Detection of seizure from the recorded EEG is a laborious, time consuming and expensive task. In this study, we propose an automated seizure detection framework to assist electroencephalographers and physicians with identification of seizures in recorded EEG signals. In addition, an automated seizure detection algorithm can be used for treatment through automatic intervention during the seizure activity and on time triggering of the injection of a radiotracer to localize the seizure activity. In this study, we developed and tested a hidden Markov factor analysis (HMFA) framework for automated seizure detection based on different features such as total effective inflow which is calculated based on connectivity measures between different sites of the brain. The algorithm was tested on long-term (2.4-7.66 days) continuous sEEG recordings from three patients and a total of 16 seizures, producing a mean sensitivity of 96.3% across all seizures, a mean specificity of 3.47 false positives per hour, and a mean latency of 3.7 seconds form the actual seizure onset. The latency was negative for a few of the seizures which implies the proposed method detects the seizure prior to its onset. This is an indication that with some extension the proposed method is capable of seizure prediction

Detecting Epileptic Regions Based on Global Brain Connectivity Patterns

We present a method to detect epileptic regions based on functional connectivity differences between individual epilepsy patients and a healthy population. Our model assumes that the global functional characteristics of these differences are shared across patients, but it allows for the epileptic regions to vary between individuals. We evaluate the detection performance against intracranial EEG observations and compare our approach with two baseline methods that use standard statistics. The baseline techniques are sensitive to the choice of thresholds, whereas our algorithm automatically estimates the appropriate model parameters and compares favorably with the best baseline results. This suggests the promise of our approach for pre-surgical planning in epilepsy.MIT/Lincoln Laboratory CollaborationNational Alliance for Medical Image Computing (U.S.) (grant (NIH NIBIB NAMIC U54-EB005149))Neuroimaging Analysis Center (U.S.) (NIH NCRR NAC P41-RR13218)Neuroimaging Analysis Center (U.S.) (NIH NIBIB NAC P41-EB-015902)National Science Foundation (U.S.) (NSF CAREER Grant 0642971

Functional neural correlates of first-episode psychoses during sensory, cognitive, language, and emotional processing

Background. Numerosi studi hanno evidenziato che i pazienti affetti da esordi sindromici dello spettro schizofrenico presentano alterazioni neurofunzionali durante l’esecuzione di compiti che coinvolgono le funzioni sensoriali, cognitive, linguistiche ed emotive. Obiettivo. Paragonare pazienti con esordio psicotico a individui sani al fine di studiare il network neurale coinvolto nelle risposte a compiti sensoriali, cognitive, linguistiche ed emotive, identificando le similarità e le differenze nelle attivazioni cerebrali correlate all’esecuzione degli stessi compiti. Metodo. Abbiamo eseguito una meta-analisi ALE utilizzando il database costruito su ventisei studi di risonanza magnetica funzionale condotti su 516 pazienti con esordio e 546 soggetti sani durante l’esecuzione di task sensoriali, cognitivi, linguistici ed emotivi. Risultati. Le analisi within-group hanno dimostrato che i controlli sani manifestavano in risposta a tutti i task attivazioni significative in un circuito bilaterale fronto-parietale, mentre i pazienti in un circuito bilaterale parietale-precentrale. Le analisi between-groups hanno evidenziato iperattivazioni del lobulo parietale inferiore di destra, del giro medio frontale sinistro e della corteccia temporale destra nei sani e del cingolo di destra nei pazienti. L’analisi condotta separatamente per gruppi di compiti ha evidenziato che la performance di task attentivo-mnestici si correlava ad attivazione di aree prefrontali nei sani e parietali bilaterali negli esordi; l’esecuzione di task emotivi si correlava ad attivazione della corteccia dorsolaterale prefrontale (DLPFC) bilaterale, della corteccia parietale destra, del cingolo di sinistra e dell’amigdala di destra nei sani e del giro fusiforme di destra nei pazienti; solo i sani hanno evidenziato attivazioni in aree corticali di sinistra incentrate sull’insula, la DLPFC e la corteccia temporale in correlazione a compiti linguistici. Conclusioni. Il risultato principale di questa meta-analisi è l’evidenza di deficit funzionale della DLPFC di sinistra in pazienti con esordio psicotico durante l’esecuzione di task sensoriali, cognitivi, linguistici ed emotivi. Il giro del cingolo di destra, le cortecce parietali e la temporale di destra hanno anch’esse un ruolo importante nella neurofisiopatologia degli esordi. Questo studio ha anche evidenziato che i pazienti attivano circuiti cerebrali diversi rispetto ai sani in risposta a compiti attentivo-mnestici (attivazione predominante in aree parietali bilaterali), emotivi (attivazione predominante nel giro fusiforme destro) e linguistici (mancata attivazione di aree corticali di sinistra).Background: Several studies reported neural functional alterations in patients with schizophrenia spectrum first-episode psychosis (FEP) during performance of tasks that involve sensory, attentional memory, language, and emotional (SAMLE) processing. Aim: To compare meta-analytically FEP and healthy control (CTR) samples regarding the circuitries engaged in responding to a set of SAMLE tasks and identifying commonalities and differences in task-related brain activations. Method: We performed an activation likelihood estimation (ALE) meta-analysis using a database built on 26 fMRI studies, conducted on 516 FEP patients and 546 CTRs during SAMLE task performance. Results: Within-Group analyses showed that the CTR group has significant SAMLE task-related cortical activations in the context of a bilateral fronto-parietal network; FEP patients showed taskrelated activations of a bilateral parietal-precentral network. Between-Groups analyses showed hyperactivation of the right inferior parietal lobule, left middle frontal gyrus, and right temporal cortex in CTRs, and hyperactivation of the right cingulate gyrus in FEP. Segregated analyses of tasks showed that brain activations to attentional and memory-related tasks mainly occurred in prefrontal areas in CTRs, and in bilateral parietal areas in FEP; emotional task-related activations concerned the bilateral dorsolateral prefrontal cortex (DLPFC), right parietal cortex, left cingulate cortex and right amygdala in CTRs, whereas in FEP the activation concerned the right fusiform gyrus; we found significant left-sided language task-related activations only in the CTR group, centred on the insula, DLPFC, and temporal cortex. Conclusions: The major finding of this study is the evidence of a functional deficit of the left DLPFC in FEP during the SAMLE task performance. A prominent role in the neuropathophysiology of FEP appears also to be played by the right dorsal anterior cingulate, bilateral parietal, and right temporal cortices. This study also underlined that FEP patients activate different circuits than CTRs in response to attentional- and memory-tasks (predominant activation of bilateral parietal areas), emotional (predominant activation of the right fusiform gyrus), and language (lack of activation of left-sided cortical areas) tasks

Brain networks underlying inhibition in Traumatic Brain Injury and in Autism Spectrum Disorders

Autism Spectrum Disorder (ASD) and Traumatic Brain Injury (TBI) are clinical populations with social cognition difficulties, exhibited by deficits in controlling impulsive or perseverative behaviors. These difficulties have been attributed to executive functioning (EF) impairments, particularly for inhibition. Thus, understanding the neural bases of inhibition is preliminary to understanding EF impairments in populations like ASD and TBI. A coordinate-based meta-analysis of functional magnetic resonance imaging (fMRI) studies was used to identify the neural basis of response inhibition in neurotypical adults to compare with TBI and ASD. Inclusion criteria for studies required reported foci for adults (17+ years of age), reported on normal mapping, and used inhibition experiential tasks that revealed activations results. Five ASD and seven TBI studies met inclusion criteria, pooling fMRI data from 1431 neurotypical subjects, 145 TBI and 71 ASD subjects engaged in inhibition tasks, yielding 98 experiments in controls and 15 experiments (9 TBI) for contrast analyses. Brain regions found to be uniquely active in the ASD or TBI and in the Control groups were further analyzed using meta-analytic connectivity modeling (MACM) to determine whether differences in these regions were functionally relevant and associated with differing behavioral patterns. The MACM analyses included 480 neurotypical experiments (6820 subjects, 7008 foci) reporting activity in the left medial frontal gyrus region of interest and 809 experiments (11568 subjects, 11855 foci) reporting activity in the right medial frontal gyrus region of interest. Results provide evidence that the brain region involved to the greatest extent for response inhibition, the medial frontal cortex, is active in individuals with TBI, with ASD and Controls. However, the groups had differences in the peaks of activity in this region. Though subtle, these differences may indicate these clinical populations are relying more on top-down, higher-level cognitive processing to accomplish response inhibition than do neurotypical controls. Results support a hypothesis that those with ASD or TBI are engaging a smaller network of brain regions, with a higher proportion of activity in the frontal lobes, and therefore less efficient than that seen in the Controls. Given the heterogeneity of TBI and ASD demographics and the variability of inhibition tasks used, these findings are speculative and require further study. This study provides support of concept for further research on functional imaging, attention, and inhibition

Effects of Transcranial Magnetic Stimulation on the Default Mode Network in Minimal Cognitive Impairment and Alzheimer's disease: An ALE meta-analysis and systematic review

openObjective: This systematic review and meta-analysis sought to comprehensively assess the efficacy of repetitive transcranial magnetic stimulation (rTMS) on the default mode network (DMN) through functional magnetic resonance imaging (fMRI) among individuals diagnosed with mild cognitive impairment (MCI) and Alzheimer's disease (AD). The primary objective was to unravel the neuroimaging mechanisms underpinning cognitive intervention. Methods: A search encompassing English articles published until July 30, 2023, was conducted across prominent databases, including PubMed, Web of Science, Embase, and Cochrane Library. The study specifically focused on randomized controlled trials utilizing resting-state fMRI to investigate the impact of rTMS within the MCI and AD populations. The analysis of fMRI data was executed using GingerALE. Results: Our meta-analysis encompassed a total of seven studies focusing on AD, collectively 116 patients in the treatment group and 90 patients in the sham group. Additionally, in MCI group comprised 34 patients in the treatment groups and 39 patients in the sham group. The combined ALE quantitative analyses on group contrasts between Alzheimer's patients and the sham group showed no significant clusters of convergence. A similar outcome was observed when conducting meta-analyses of the MCI group. The restricted pool of eligible studies may have hindered our ability to detect meaningful clusters of convergence. Conclusions: The outcomes of this meta-analysis and systematic review collectively underscore the potential effectiveness and safety of rTMS intervention in addressing the needs of patients coping with MCI and AD. These improvements could likely be attributed to the favorable modulation that rTMS imparts upon spontaneous neural activity and cognitive networks. By elucidating the intricate neural mechanisms involved, this study contributes insights into the burgeoning field of cognitive intervention strategie