Specific Brain Networks Research Articles

Electrical status epilepticus in sleep affects intrinsically connected networks in patients with benign childhood epilepsy with centrotemporal spikes

BackgroundAlthough outcomes of benign childhood epilepsy with centrotemporal spikes (BECTS) are frequently excellent, some atypical forms of BECTS, especially electrical status epilepticus in sleep (ESES), are characterized by worse outcomes and negative impacts on cognitive development. MethodsTo explore specific ESES-related brain networks in patients with BECTS, we used resting-state functional magnetic resonance imaging (fMRI) to scan patients with BECTS with ESES (n = 9), patients with BECTS without ESES (n = 17), and healthy controls (n = 36). Unbiased seed-based whole-brain functional connectivity (FC) was adopted to explore the connectivity mode of three resting-state cerebral networks: the default mode network (DMN), salience network (SN), and central executive network (CEN). ResultsCompared with the other two groups, patients with BECTS with ESES showed FC in the SN or in the CEN decreased, but not in the DMN. Moreover, we found the FC in the CEN in patients with BECTS without ESES decreased when compared with controls. Our currently intrinsically defined anticorrelated networks strength was disrupted in BECTS and connote greater deactivation than the results from FC for a seed region in children with BECTS. ConclusionThese results indicated that children with BECTS with ESES showed brain activity altered in the CEN and the SN. The difference of impairment in the SN and CEN may lead to improve the understanding of the underlying neuropathophysiology, and to assess the activity of patients with BECTS with ESES, which is crucial for measuring disease activity, improving patient care, and assessing the effect of antiepilepsy therapy.

Random Forest Classification of Alcohol Use Disorder Using EEG Source Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures

Individuals with alcohol use disorder (AUD) manifest a variety of impairments that can be attributed to alterations in specific brain networks. The current study aims to identify features of EEG-based functional connectivity, neuropsychological performance, and impulsivity that can classify individuals with AUD (N = 30) from unaffected controls (CTL, N = 30) using random forest classification. The features included were: (i) EEG source functional connectivity (FC) of the default mode network (DMN) derived using eLORETA algorithm, (ii) neuropsychological scores from the Tower of London test (TOLT) and the visual span test (VST), and (iii) impulsivity factors from the Barratt impulsiveness scale (BIS). The random forest model achieved a classification accuracy of 80% and identified 29 FC connections (among 66 connections per frequency band), 3 neuropsychological variables from VST (total number of correctly performed trials in forward and backward sequences and average time for correct trials in forward sequence) and all four impulsivity scores (motor, non-planning, attentional, and total) as significantly contributing to classifying individuals as either AUD or CTL. Although there was a significant age difference between the groups, most of the top variables that contributed to the classification were not significantly correlated with age. The AUD group showed a predominant pattern of hyperconnectivity among 25 of 29 significant connections, indicating aberrant network functioning during resting state suggestive of neural hyperexcitability and impulsivity. Further, parahippocampal hyperconnectivity with other DMN regions was identified as a major hub region dysregulated in AUD (13 connections overall), possibly due to neural damage from chronic drinking, which may give rise to cognitive impairments, including memory deficits and blackouts. Furthermore, hypoconnectivity observed in four connections (prefrontal nodes connecting posterior right-hemispheric regions) may indicate a weaker or fractured prefrontal connectivity with other regions, which may be related to impaired higher cognitive functions. The AUD group also showed poorer memory performance on the VST task and increased impulsivity in all factors compared to controls. Features from all three domains had significant associations with one another. These results indicate that dysregulated neural connectivity across the DMN regions, especially relating to hyperconnected parahippocampal hub as well as hypoconnected prefrontal hub, may potentially represent neurophysiological biomarkers of AUD, while poor visual memory performance and heightened impulsivity may serve as cognitive-behavioral indices of AUD.

Uncovering complex central autonomic networks at rest: a functional magnetic resonance imaging study on complex cardiovascular oscillations.

This study aims to uncover brain areas that are functionally linked to complex cardiovascular oscillations in resting-state conditions. Multi-session functional magnetic resonance imaging (fMRI) and cardiovascular data were gathered from 34 healthy volunteers recruited within the human connectome project (the '100-unrelated subjects' release). Group-wise multi-level fMRI analyses in conjunction with complex instantaneous heartbeat correlates (entropy and Lyapunov exponent) revealed the existence of a specialized brain network, i.e. a complex central autonomic network (CCAN), reflecting what we refer to as complex autonomic control of the heart. Our results reveal CCAN areas comprised the paracingulate and cingulate gyri, temporal gyrus, frontal orbital cortex, planum temporale, temporal fusiform, superior and middle frontal gyri, lateral occipital cortex, angular gyrus, precuneous cortex, frontal pole, intracalcarine and supracalcarine cortices, parahippocampal gyrus and left hippocampus. The CCAN visible at rest does not include the insular cortex, thalamus, putamen, amygdala and right caudate, which are classical CAN regions peculiar to sympatho-vagal control. Our results also suggest that the CCAN is mainly involved in complex vagal control mechanisms, with possible links with emotional processing networks.

Random Forest Classification of Alcohol Use Disorder Using fMRI Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures.

Individuals with alcohol use disorder (AUD) are known to manifest a variety of neurocognitive impairments that can be attributed to alterations in specific brain networks. The current study aims to identify specific features of brain connectivity, neuropsychological performance, and impulsivity traits that can classify adult males with AUD (n = 30) from healthy controls (CTL, n = 30) using the Random Forest (RF) classification method. The predictor variables were: (i) fMRI-based within-network functional connectivity (FC) of the Default Mode Network (DMN), (ii) neuropsychological scores from the Tower of London Test (TOLT), and the Visual Span Test (VST), and (iii) impulsivity factors from the Barratt Impulsiveness Scale (BIS). The RF model, with a classification accuracy of 76.67%, identified fourteen DMN connections, two neuropsychological variables (memory span and total correct scores of the forward condition of the VST), and all impulsivity factors as significantly important for classifying participants into either the AUD or CTL group. Specifically, the AUD group manifested hyperconnectivity across the bilateral anterior cingulate cortex and the prefrontal cortex as well as between the bilateral posterior cingulate cortex and the left inferior parietal lobule, while showing hypoconnectivity in long-range anterior–posterior and interhemispheric long-range connections. Individuals with AUD also showed poorer memory performance and increased impulsivity compared to CTL individuals. Furthermore, there were significant associations among FC, impulsivity, neuropsychological performance, and AUD status. These results confirm the previous findings that alterations in specific brain networks coupled with poor neuropsychological functioning and heightened impulsivity may characterize individuals with AUD, who can be efficiently identified using classification algorithms such as Random Forest.

Pathologic tearfulness after limbic encephalitis: A novel disorder and its neural basis.

ObjectiveWe investigated the nature and neural foundations of pathologic tearfulness in a uniquely large cohort of patients who had presented with autoimmune limbic encephalitis (aLE).MethodsWe recruited 38 patients (26 men, 12 women; median age 63.06 years; interquartile range [IQR] 16.06 years) in the postacute phase of aLE who completed questionnaires probing emotion regulation. All patients underwent structural/functional MRI postacutely, along with 67 age- and sex-matched healthy controls (40 men, 27 women; median age 64.70 years; IQR 19.87 years). We investigated correlations of questionnaire scores with demographic, clinical, neuropsychological, and brain imaging data across patients. We also compared patients diagnosed with pathologic tearfulness and those without, along with healthy controls, on gray matter volume, resting-state functional connectivity, and activity.ResultsPathologic tearfulness was reported by 50% of the patients, while no patient reported pathologic laughing. It was not associated with depression, impulsiveness, memory impairment, executive dysfunction in the postacute phase, or amygdalar abnormalities in the acute phase. It correlated with changes in specific emotional brain networks: volume reduction in the right anterior hippocampus, left fusiform gyrus, and cerebellum, abnormal hippocampal resting-state functional connectivity with the posteromedial cortex and right middle frontal gyrus, and abnormal hemodynamic activity in the left fusiform gyrus, right inferior parietal lobule, and ventral pons.ConclusionsPathologic tearfulness is common following aLE, is not a manifestation of other neuropsychiatric features, and reflects abnormalities in networks of emotion regulation beyond the acute hippocampal focus. The condition, which may also be present in other neurologic disorders, provides novel insights into the neural basis of affective control and its dysfunction in disease.

Functional connectivity of specific brain networks related to social and communication dysfunction in adolescents with attention-deficit hyperactivity disorder

BackgroundAdolescents with attention-deficit hyperactivity disorder (ADHD) may have impaired social cognition and communication. However, the functioning of the brain networks involved in the social cognition and communication impairment in ADHD patients remains unclear. MethodsIn total, 18 adolescents with ADHD and 16 age- and sex-matched typically developing adolescents (controls)—all of whom underwent a brain magnetic resonance imaging examination—were enrolled. Their parents filled out Swanson, Nolan, and Pelham IV (SNAP-IV) and Social Responsiveness Scale (SRS) questionnaires. Functional connectivity analyses based on the default mode network, frontoparietal network, and cinguloopercular network were performed. ResultsCompared with controls, adolescents with ADHD exhibited higher total and subscale scores on SNAP-IV and SRS. Higher SNAP-IV and SRS scores were associated with higher functional connectivity between the default mode network (ventromedial prefrontal cortex) and cinguloopercular network (anterior insula) and between the FPN (dorsolateral and prefrontal cortex) and cinguloopercular network, but with lower functional connectivity between the default mode network (posterior cingulate cortex) and frontoparietal network (inferior parietal lobule) and between the default mode network (precuneus) and cinguloopercular network (temporoparietal junction). DiscussionSocial cognition and communication impairment and ADHD may commonly share the aberrant functional connectivity in the default mode network, frontoparietal network, and cinguloopercular network.

Spatial patterns of tau deposition are associated with amyloid, ApoE, sex, and cognitive decline in older adults

PurposeThe abnormal deposition of tau begins before the onset of clinical symptoms and seems to target specific brain networks. The aim of this study is to identify the spatial patterns of tau deposition in cognitively normal older adults and assess whether they are related to amyloid-β (Aβ), APOE, sex, and longitudinal cognitive decline.MethodsWe included 114 older adults with cross-sectional flortaucipir (FTP) and Pittsburgh Compound-B PET in addition to longitudinal cognitive testing. A voxel-wise independent component analysis was applied to FTP images to identify the spatial patterns of tau deposition. We then assessed whether tau within these patterns differed by Aβ status, APOE genotype, and sex. Linear mixed effects models were built to test whether tau in each component predicted cognitive decline. Finally, we ordered the spatial components based on the frequency of high tau deposition to model tau spread.ResultsWe found 10 biologically plausible tau patterns in the whole sample. There was greater tau in medial temporal, occipital, and orbitofrontal components in Aβ-positive compared with Aβ-negative individuals; in the parahippocampal component in ε3ε3 compared with ε2ε3 carriers; and in temporo-parietal and anterior frontal components in women compared with men. Higher tau in temporal and frontal components predicted longitudinal cognitive decline in memory and executive functions, respectively. Tau deposition was most frequently observed in medial temporal and ventral cortical areas, followed by lateral and primary areas.ConclusionsThese findings suggest that the spatial patterns of tau in asymptomatic individuals are clinically meaningful and are associated with Aβ, APOE ε2ε3, sex and cognitive decline. These patterns could be used to predict the regional spread of tau and perform in vivo tau staging in older adults.

Chapter 21 - Real-time fMRI for brain-computer interfacing

Brain-computer interfaces (BCIs) based on functional magnetic resonance imaging (fMRI) provide an important complement to other noninvasive BCIs. While fMRI has several disadvantages (being nonportable, methodologically challenging, costly, and noisy), it is the only method providing high spatial resolution whole-brain coverage of brain activation. These properties allow relating mental activities to specific brain regions and networks providing a transparent scheme for BCI users to encode information and for real-time fMRI BCI systems to decode the intents of the user. Various mental activities have been used successfully in fMRI BCIs so far that can be classified into the four categories: (a) higher-order cognitive tasks (e.g., mental calculation), (b) covert language-related tasks (e.g., mental speech and mental singing), (c) imagery tasks (motor, visual, auditory, tactile, and emotion imagery), and (d) selective attention tasks (visual, auditory, and tactile attention). While the ultimate spatial and temporal resolution of fMRI BCIs is limited by the physiologic properties of the hemodynamic response, technical and analytical advances will likely lead to substantially improved fMRI BCIs in the future using, for example, decoding of imagined letter shapes at 7T as the basis for more "natural" communication BCIs.

An Operant Conditioning Model Combined with a Chemogenetic Approach to Study the Neurobiology of Food Addiction in Mice.

The study of food addiction comprises 3 hallmarks that include the persistence to response without an outcome, the strong motivation for palatable food, and the loss of inhibitory control over food intake that leads to compulsive behavior in addicted individuals. The complex multifactorial nature of this disorder and the unknown neurobiological mechanistic correlation explains the lack of effective treatments. Our operant conditioning model allows deciphering why some individuals are vulnerable and develop food addiction while others are resilient and do not. It is a translational approach since it is based on the Diagnostic and Statistical Manual of Mental Disorders 5th edition (DSM-5) and the Yale Food Addiction Scale (YFAS 2.0). This model allows to evaluate the addiction criteria in 2 time-points at an early and a late period by grouping them into 1) persistence to response during a period of non-availability of food, 2) motivation for food with a progressive ratio, and 3) compulsivity when the reward is associated with a punishment such as an electric foot-shock. The advantage of this model is that it allows us to measure 4 phenotypic traits suggested as predisposing factors related to vulnerability to addiction. Also, it is possible to evaluate the long food addiction mouse model with mice genetically modified. Importantly, the novelty of this protocol is the adaptation of this food addiction model to a short protocol to evaluate genetic manipulations targeting specific brain circuitries by using a chemogenetic approach that could promote the rapid development of this addictive behavior. These adaptations lead to a short food addiction mouse protocol, in which mice follow the same behavioral procedure of the early period in the long food addiction protocol with some variations due to the surgical viral vector injection. To our knowledge, there is no paradigm in mice allowing us to study the combination of such a robust behavioral approach that allows uncovering the neurobiology of food addiction at the brain circuit level. We can study using this protocol if modifying the excitability of a specific brain network confers resilience or vulnerability to developing food addiction. Understanding these neurobiological mechanisms is expected to help to find novel and efficient interventions to battle food addiction.

Coactivation pattern analysis reveals altered salience network dynamics in children with autism spectrum disorder.

Brain connectivity studies of autism spectrum disorder (ASD) have historically relied on static measures of functional connectivity. Recent work has focused on identifying transient configurations of brain activity, yet several open questions remain regarding the nature of specific brain network dynamics in ASD. We used a dynamic coactivation pattern (CAP) approach to investigate the salience/midcingulo-insular (M-CIN) network, a locus of dysfunction in ASD, in a large multisite resting-state fMRI dataset collected from 172 children (ages 6–13 years; n = 75 ASD; n = 138 male). Following brain parcellation by using independent component analysis, dynamic CAP analyses were conducted and k-means clustering was used to determine transient activation patterns of the M-CIN. The frequency of occurrence of different dynamic CAP brain states was then compared between children with ASD and typically developing (TD) children. Dynamic brain configurations characterized by coactivation of the M-CIN with central executive/lateral fronto-parietal and default mode/medial fronto-parietal networks appeared less frequently in children with ASD compared with TD children. This study highlights the utility of time-varying approaches for studying altered M-CIN function in prevalent neurodevelopmental disorders. We speculate that altered M-CIN dynamics in ASD may underlie the inflexible behaviors commonly observed in children with the disorder.

White matter tracts involved by deep brain stimulation of the subthalamic nucleus in Parkinson’s disease: a connectivity study based on preoperative diffusion tensor imaging tractography

Purpose: To depict the specific brain networks that are modulated by deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson’s disease (PD), using diffusion tensor imaging-based fibre tractography (DTI-FT).Materials and methods: Nine patients who received bilateral STN-DBS for PD were included. Electrodes were localized by co-registering preoperative magnetic resonance imaging and postoperative computed tomography. The volume of tissue activated (VTA) was estimated as an isotropic, spherical electric field distribution centred at each effective electrode contact’s centroid coordinates, taking into account individual stimulation parameters (i.e. voltage, impedance). Brain connectivity analysis was undertaken using a deterministic DTI-FT method, seeded from a single region of interest corresponding to the VTA. The labelling of the reconstructed white matter fibre tracts relied on their path and (sub)cortical termination territories.Results: Six months after surgery, we observed a statistically significant reduction in both the Unified Parkinson Disease Rating Scale part III and L-dopa equivalent daily dose. Areas consistently connected to the VTA included the brainstem (100%), cerebellum (94%), dorsal (i.e. supplementary motor area) and lateral premotor cortex (94%), and primary motor cortex (72%). An involvement of the hyperdirect pathway (HDP) connecting the STN and the (pre)motor cortex was demonstrated.Conclusions: The connectivity patterns observed in this study suggest that the therapeutic effects of STN-DBS are mediated through the modulation of distributed, large-scale motor networks. Specifically, the depiction of projection neurons connecting the stimulated area/STN to the (pre)motor cortex, reinforce the growing evidence that the HDP might be a potential therapeutic target in PD. If further replicated, these findings could raise the possibility that DTI-FT reconstruction of the HDP may critically improve DBS targeting and stimulation parameters selection, through the development of programming tools that incorporate VTA modelling and patient-specific DTI-FT data.

Synaptic Plasticity Shapes Brain Connectivity: Implications for Network Topology.

Studies of brain network connectivity improved understanding on brain changes and adaptation in response to different pathologies. Synaptic plasticity, the ability of neurons to modify their connections, is involved in brain network remodeling following different types of brain damage (e.g., vascular, neurodegenerative, inflammatory). Although synaptic plasticity mechanisms have been extensively elucidated, how neural plasticity can shape network organization is far from being completely understood. Similarities existing between synaptic plasticity and principles governing brain network organization could be helpful to define brain network properties and reorganization profiles after damage. In this review, we discuss how different forms of synaptic plasticity, including homeostatic and anti-homeostatic mechanisms, could be directly involved in generating specific brain network characteristics. We propose that long-term potentiation could represent the neurophysiological basis for the formation of highly connected nodes (hubs). Conversely, homeostatic plasticity may contribute to stabilize network activity preventing poor and excessive connectivity in the peripheral nodes. In addition, synaptic plasticity dysfunction may drive brain network disruption in neuropsychiatric conditions such as Alzheimer’s disease and schizophrenia. Optimal network architecture, characterized by efficient information processing and resilience, and reorganization after damage strictly depend on the balance between these forms of plasticity.

Functional Connectivity in Neurodegenerative Disorders: Alzheimer's Disease and Frontotemporal Dementia.

Neurodegenerative disorders are a growing cause of morbidity and mortality worldwide. Onset is typically insidious and clinical symptoms of behavioral change, memory loss, or cognitive dysfunction may not be evident early in the disease process. Efforts have been made to discover biomarkers that allow for earlier diagnosis of neurodegenerative disorders, to initiate treatment that may slow the course of clinical deterioration. Neuronal dysfunction occurs earlier than clinical symptoms manifest. Thus, assessment of neuronal function using functional brain imaging has been examined as a potential biomarker. While most early studies used task-functional magnetic resonance imaging (fMRI), with the more recent technique of resting-state fMRI, "intrinsic" relationships between brain regions or brain networks have been studied in greater detail in neurodegenerative disorders. In Alzheimer's disease, the most common neurodegenerative disorder, and frontotemporal dementia, another of the common dementias, specific brain networks may be particularly susceptible to dysfunction. In this review, we highlight the major findings of functional connectivity assessed by resting state fMRI in Alzheimer's disease and frontotemporal dementia.

Predicting the impact of sodium channel mutations in human brain disease.

SummaryGenetic alteration of the sodium channel provides a remarkable opportunity to understand how epilepsy and its comorbidities arise from a molecular disease of excitable membranes, and a chance to create a better future for children with epileptic encephalopathy. In a single cell, the channel reliably acts as a voltage‐sensitive switch, enabling axon impulse firing, whereas at a network level, it becomes a variable rheostat for regulating dynamic patterns of neuronal oscillations, including those underlying cognitive development, seizures, and even premature lethality. Despite steady progress linking genetic variation of the channels with distinctive clinical syndromes, our understanding of the intervening biologic complexity underlying each of them is only just beginning. More research on the functional contribution of individual channel subunits to specific brain networks and cellular plasticity in the developing brain is needed before we can reliably advance from precision diagnosis to precision treatment of inherited sodium channel disorders.

Diet as connecting factor: Functional brain connectivity in relation to food intake and sucrose tasting, assessed with resting-state functional MRI in rats.

Eating disorders and obesity form a major health problem in Western Society. To be able to provide adequate treatment and prevention, it is necessary to understand the neural mechanisms underlying the development of eating disorders and obesity. Specific brain networks have been shown to be involved in feeding behavior. We therefore hypothesized that functional connectivity in neural networks involved in feeding behavior is dependent on the status of homeostatic energy balance, thus on being hungry or satiated. To test our hypothesis, we measured functional connectivity and amplitudes of neural signals within neural networks in relation to food intake and sucrose tasting in rats. Therefore, 16 male Wistar rats, of which eight were food‐restricted and eight were satiated, underwent resting‐state functional magnetic resonance imaging (rs‐fMRI) at 9.4 T. Subsequently, half of these animals underwent a sucrose tasting procedure followed by a second rs‐fMRI scan. Functional connectivity and amplitude of low‐frequency signal fluctuations were statistically analyzed in a linear mixed model. Although we did not detect a significant effect of food intake on functional connectivity before sucrose tasting, there was a trend toward interaction between group (satiated vs. hungry) and treatment (sucrose tasting). Functional connectivity between feeding‐related regions tended to decrease stronger upon sucrose tasting in satiated rats as compared to food‐restricted rats. Furthermore, rs‐fMRI signal amplitudes decreased stronger upon sucrose tasting in satiated rats, as compared to food‐restricted rats. These findings indicate that food intake and sucrose tasting can affect functional network organization, which may explain the specific patterns in feeding behavior.

Looking beyond the face area: lesion network mapping of prosopagnosia.

Damage to the right fusiform face area can disrupt the ability to recognize faces, a classic example of how damage to a specialized brain region can disrupt a specialized brain function. However, similar symptoms can arise from damage to other brain regions, and face recognition is now thought to depend on a distributed brain network. The extent of this network and which regions are critical for facial recognition remains unclear. Here, we derive this network empirically based on lesion locations causing clinically significant impairments in facial recognition. Cases of acquired prosopagnosia were identified through a systematic literature search and lesion locations were mapped to a common brain atlas. The network of brain regions connected to each lesion location was identified using resting state functional connectivity from healthy participants (n = 1000), a technique termed lesion network mapping. Lesion networks were overlapped to identify connections common to lesions causing prosopagnosia. Reproducibility was assessed using split-half replication. Specificity was assessed through comparison with non-specific control lesions (n = 135) and with control lesions associated with symptoms other than prosopagnosia (n = 155). Finally, we tested whether our facial recognition network derived from clinically evident cases of prosopagnosia could predict subclinical facial agnosia in an independent lesion cohort (n = 31). Our systematic literature search identified 44 lesions causing prosopagnosia, only 29 of which intersected the right fusiform face area. However, all 44 lesion locations fell within a single brain network defined by connectivity to the right fusiform face area. Less consistent connectivity was found to other face-selective regions. Surprisingly, all 44 lesion locations were also functionally connected, through negative correlation, with regions in the left frontal cortex. This connectivity pattern was highly reproducible and specific to lesions causing prosopagnosia. Positive connectivity to the right fusiform face area and negative connectivity to left frontal regions were independent predictors of prosopagnosia and predicted subclinical facial agnosia in an independent lesion cohort. We conclude that lesions causing prosopagnosia localize to a single functionally connected brain network defined by connectivity to the right fusiform face area and to left frontal regions. Implications of these findings for models of facial recognition deficits are discussed.

Pharmaco-fMRI: A Tool to Predict the Response to Antiepileptic Drugs in Epilepsy.

Pharmacological treatment with antiepileptic medications (AEDs) in epilepsy is associated with a variety of neurocognitive side effects. However, the mechanisms underlying these side effects, and why certain brain anatomies are more affected still remain poorly understood. Advanced functional magnetic resonance imaging (fMRI) methods, such as pharmaco-fMRI, can investigate medication-related effects on brain activities using task and resting state fMRI and showing reproducible activation and deactivation patterns. This methodological approach has been used successfully to complement neuropsychological studies of AEDs. Here we review pharmaco-fMRI studies in people with epilepsy targeting the most-widely prescribed AEDs. Pharmco-fMRI has advanced our understanding of the impact of AEDs on specific brain networks and thus may provide potential biomarkers to move beyond the current “trial and error” approach when commencing anti-epileptic medication.

Mental Imagery and Brain Regulation-New Links Between Psychotherapy and Neuroscience.

Mental imagery is a promising tool and mechanism of psychological interventions, particularly for mood and anxiety disorders. In parallel developments, neuromodulation techniques have shown promise as add-on therapies in psychiatry, particularly non-invasive brain stimulation for depression. However, these techniques have not yet been combined in a systematic manner. One novel technology that may be able to achieve this is neurofeedback, which entails the self-regulation of activation in specific brain areas or networks (or the self-modulation of distributed activation patterns) by the patients themselves, through real-time feedback of brain activation (for example, from functional magnetic resonance imaging). One of the key mechanisms by which patients learn such self-regulation is mental imagery. Here, we will first review the main mental imagery approaches in psychotherapy and the implicated brain networks. We will then discuss how these networks can be targeted with neuromodulation (neurofeedback or non-invasive or invasive brain stimulation). We will review the clinical evidence for neurofeedback and discuss possible ways of enhancing it through systematic combination with psychological interventions, with a focus on depression, anxiety disorders, and addiction. The overarching aim of this perspective paper will be to open a debate on new ways of developing neuropsychotherapies.

The oblique effect: The relationship between profiles of visuospatial preference, cognition, and brain connectomics in older adults

The oblique effect (OE) describes the visuospatial advantage for identifying stimuli oriented horizontally or vertically rather than diagonally; little is known about brain aging and the OE. We investigated this relationship using the Judgment of Line Orientation (JLO) in 107 older adults (∼age = 67.8 ± 6.6; 51% female) together with neuropsychological tests of executive functioning (EF), attention/information processing (AIP), and neuroimaging. Only JLO lines falling between 36-54° or 126–144° were considered oblique. To quantify the oblique effect, we calculated z-scores for oblique errors (zOblique = #oblique errors/#oblique lines), and similarly, horizontal + vertical line errors (zHV), and a composite measure of oblique relative to HV errors (zOE). Composite z-scores of EF and AIP reflected domains associated with JLO performance. Graph theory analysis integrated T1-derived volumetry and diffusion MRI-derived white matter tractography into connectivity matrices analyzed for select network properties. Participants produced more zOblique than zHV errors (p < 0.001). Age was not associated with zOE adjusting for sex, education, and MMSE. Similarly adjusted linear regression models revealed that lower EF was associated with a larger oblique effect (p < 0.001). Modular analyses of neural connectivity revealed a differential patterns of network affiliation that varied by high versus low group status determined via median split of zOblique and zHV errors, separately. Older adults exhibit the oblique effect and it is associated with specific cognitive processes and regional brain networks that may facilitate future investigations of visuospatial preference in aging.

Neuroimaging correlates of cognitive changes after bariatric surgery.

Obesity has been associated with cognitive deficits and increased risk for developing dementia. Bariatric surgery may result in improved cognitive function; however, the underlying structural and functional brain correlates are unclear. This longitudinal study explores the hypothesis that specific brain regions and networks underlie cognitive changes after bariatric surgery. University Hospital, United States. Seventeen patients were recruited for this prospective cohort study, including 9 patients undergoing bariatric surgery, and 8 age-, sex-, and education level-matched healthy, nonobese control patients. Bariatric patients underwent longitudinal neuropsychologic tests and magnetic resonance imaging (MRI) scans both before and 6 months after surgery. One patient was lost to follow-up. The same neuropsychologic tests and MRI scans were performed for control patients. Differences in MRI and neuropsychologic testing between bariatric patients and control patients, and longitudinal changes within bariatric patients were assessed. At baseline, bariatric patients demonstrated deficits in cognitive function relative to control patients, including pattern comparison (P = .009) and picture sequence memory (P = .004), which improved after significant weight loss. Baseline cognitive deficits in bariatric patients were accompanied by significantly lower left executive control network connectivity on resting-state functional MRI relative to control patients (P = .028), but differences resolved or diminished after bariatric surgery. Longitudinal improvements in pattern comparison performance correlated significantly with increases in left executive control network connectivity (r = .819; P = .013). No significant group or longitudinal differences were found in brain perfusion or brain white matter lesions. Individuals with obesity undergoing bariatric surgery exhibit deficits in cognitive function and specific alterations of brain networks; however, cognitive performance can improve, and executive control network connectivity can increase after weight loss from bariatric surgery.