Mechanisms of cognitive-behavioral therapy for obsessive-compulsive disorder involve robust and extensive increases in brain network connectivity.

Cognitive-behavioral therapy (CBT) is effective for obsessive compulsive disorder (OCD); however, little is understood about its mechanisms related to brain network connectivity. We examined connectivity changes from resting-state functional magnetic resonance imaging data pre-to-post-CBT in 43 OCD participants, randomized to receive either 4 weeks of intensive CBT or 4 weeks waitlist followed by 4 weeks of CBT, and 24 healthy controls before and after 4 weeks of no treatment. Network-based-statistic analysis revealed large-magnitude increases in OCD connectivity in eight networks. Strongest increases involved connectivity between the cerebellum and caudate/putamen, and between the cerebellum and dorsolateral/ventrolateral prefrontal cortices. Connectivity increases were associated with increased resistance to compulsions. Mechanisms of CBT may involve enhanced cross-network integration, both within and outside of classical cortico-striatal-thalamo-cortical regions; those involving cerebellar to striatal and prefrontal regions may reflect acquisition of new non-compulsive goal-directed behaviors and thought patterns. Our findings have implications for identifying targets for enhancing treatment efficacy and monitoring treatment progress

Alzheimer’s Disease Diagnosis Using Machine Learning: A Survey

Alzheimer’s is a neurodegenerative disorder affecting the central nervous system and cognitive processes, explicitly impairing detailed mental analysis. Throughout this condition, the affected individual’s cognitive abilities to process and analyze information gradually deteriorate, resulting in mental decline. In recent years, there has been a notable increase in endeavors aimed at identifying Alzheimer’s disease and addressing its progression. Research studies have demonstrated the significant involvement of genetic factors, stress, and nutrition in developing this condition. The utilization of computer-aided analysis models based on machine learning and artificial intelligence has the potential to significantly enhance the exploration of various neuroimaging methods and non-image biomarkers. This study conducts a comparative assessment of more than 80 publications that have been published since 2017. Alzheimer’s disease detection is facilitated by utilizing fundamental machine learning architectures such as support vector machines, decision trees, and ensemble models. Furthermore, around 50 papers that utilized a specific architectural or design approach concerning Alzheimer’s disease were examined. The body of literature under consideration has been categorized and elucidated through the utilization of data-related, methodology-related, and medical-fostering components to illustrate the underlying challenges. The conclusion section of our study encompasses a discussion of prospective avenues for further investigation and furnishes recommendations for future research activities on the diagnosis of Alzheimer’s disease

Resting state functional connectivity in the default mode network and aerobic exercise in young adults

Around the world Alzheimer’s Disease (AD) is on the rise. Previous studies have shown the default mode network (DMN) sees changes with AD progression as the disease erodes away cortical areas. Aerobic exercise with significant increases to cardiorespiratory fitness could show neuro-protective changes to delay AD. This study will explore if functional connectivity changes in the DMN can be seen in a young adult sample by using group independent component analysis through FSL MELODIC. The young adult sample of 19 were selected from a larger study at the Brain Plasticity and Neuroimaging Laboratory at Boston University. The participants engaged in a twelve-week exercise intervention in either a strength training or aerobic training group. They also completed pre-intervention and post-intervention resting-state fMRI scans to evaluate change in functional connectivity in the default mode network. Cardiorespiratory fitness was assessed using a modified Balke protocol with pre-intervention and post-intervention VO2 max percentiles being used. Through two repeated-measure ANOVA analyses, this study found no significant increase in mean functional connectivity or cardiorespiratory fitness in the young adult sample. While improvements in mean VO2 max percentile and functional connectivity would have been seen with a larger sample size, this study adds to the literature by suggesting if fitness does not improve significantly, neither will functional connectivity in the default mode network

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Bariatric surgery and brain health: A longitudinal observational study investigating the effect of surgery on cognitive function and gray matter volume

Dietary modifications leading to weight loss have been suggested as a means to improve brain health. In morbid obesity, bariatric surgery (BARS)—including different procedures, such as vertical sleeve gastrectomy (VSG), gastric banding (GB), or Roux-en-Y gastric bypass (RYGB) surgery—is performed to induce rapid weight loss. Combining reduced food intake and malabsorption of nutrients, RYGB might be most effective, but requires life-long follow-up treatment. Here, we tested 40 patients before and six months after surgery (BARS group) using a neuropsychological test battery and compared them with a waiting list control group. Subsamples of both groups underwent structural MRI and were examined for differences between surgical procedures. No substantial differences between BARS and control group emerged with regard to cognition. However, larger gray matter volume in fronto-temporal brain areas accompanied by smaller volume in the ventral striatum was seen in the BARS group compared to controls. RYGB patients compared to patients with restrictive treatment alone (VSG/GB) had higher weight loss, but did not benefit more in cognitive outcomes. In sum, the data of our study suggest that BARS might lead to brain structure reorganization at long-term follow-up, while the type of surgical procedure does not differentially modulate cognitive performance

2008 Progress Report on Brain Research

Highlights new research on various disorders, nervous system injuries, neuroethics, neuroimmunology, pain, sense and body function, stem cells and neurogenesis, and thought and memory. Includes essays on arts and cognition and on deep brain stimulation

A survey of deep learning models in medical therapeutic areas.

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Neuropsychological Generation of Source Amnesia: An Episodic Memory Disorder of the Frontal Brain

Source amnesia is an explicit memory (declarative) disorder, particularly episodic, where source or contextual information concerning facts is severely distorted and/or unable to be recalled. This paper reviews the literature on source amnesia, including memory distrust syndrome, and its accepted correlation with the medial diencephalic system and the temporal lobes, and the suggested linkage between the frontal lobes, including special interest with the prefrontal cortex. Posthypnotic induction was the first presentation of source amnesia identified in the literature. The Wisconsin Cart Sorting Test (WCST), Positron Emission Topography (PET), Phonemic Verbal Fluency Test, Stroop Color Word Interference Test, and explicit and implicit memory tests are defined and linked to empirical research on amnesiacs

Brain Training and Meditation’s Effects on Memory in Subjects with Vascular Cognitive Impairment

Vascular Dementia (VaD) is an important public health concern, which causes significant morbidity and mortality amongst populations around the world. With the increases in average age of individuals and prevalence of cardiovascular risk factors, the incidence of vascular cognitive impairment (VCI) and VaD are on the rise. Most of this increase will come from cerebral small vessel disease (CSVD) as treatment for large vessel disease improves. Yet, very few interventions are recommended for CSVD beyond control of risk factors. In this thesis, we propose a non-pharmacological intervention, which we believe may address executive dysfunction in VCI due to CSVD. CSVD impairs functional frontal-subcortical connectivity and results in cognitive and functional impairments. Given the plasticity in these circuits, despite old age, cognitive training may be a good candidate for improving cognition in CSVD. However, previous studies have suffered from heterogeneity of pathologies in VCI by including both large and small vessel disease. Furthermore, they have often not considered the effects of anxiety and depression, which we aim to exclude from the study. Finally, these studies do not use validated composite scores as a primary endpoint and currently do not use any biomarkers to follow the progress of subjects. In this study, we aim to partially address these shortcomings and offer a more rigorous approach to cognitive training