Relevant Brain Research Articles

In vivo imaging of mGlu5 receptor expression in humans with Fragile X Syndrome towards development of a potential biomarker

Fragile X Syndrome (FXS) is a neurodevelopmental disorder caused by silencing of the Fragile X Mental Retardation (FMR1) gene. The resulting loss of Fragile X Mental Retardation Protein (FMRP) leads to excessive glutamate signaling via metabotropic glutamate subtype 5 receptors (mGluR5) which has been implicated in the pathogenesis of the disorder. In the present study we used the radioligand 3-[18F]fluoro-5-(2-pyridinylethynyl)benzonitrile ([18F]FPEB) in simultaneous PET-MR imaging of males with FXS and age- and gender-matched controls to assess the availability of mGlu5 receptors in relevant brain areas. Patients with FXS showed lower [18F]FPEB binding potential (p < 0.01), reflecting reduced mGluR5 availability, than the healthy controls throughout the brain, with significant group differences in insula, anterior cingulate, parahippocampal, inferior temporal and olfactory cortices, regions associated with deficits in inhibition, memory, and visuospatial processes characteristic of the disorder. The results are among the first to provide in vivo evidence of decreased availability of mGluR5 in the brain in individuals with FXS than in healthy controls. The consistent results across the subjects, despite the tremendous challenges with neuroimaging this population, highlight the robustness of the protocol and support for its use in drug occupancy studies; extending our radiotracer development and application efforts from mice to humans.

Fuzzy Clustering Algorithm-Segmented MRI Images in Analysis of Effects of Mental Imagery on Neurorehabilitation of Stroke Patients

The study focused on the automatic segmentation of Magnetic Resonance Imaging (MRI) images of stroke patients and the therapeutic effects of Mental Imagery on motor and neurological functions after stroke. First, the traditional fuzzy c-means (FCM) algorithm was optimized, and the optimized one was defined as filter-based FCM (FBFCM). 62 stroke patients were selected as the research subjects and randomly divided into the experimental group and the control group. The control group accepted the conventional rehabilitation training, and the experimental group accepted Mental Imagery on the basis of the control group. They all had the MRI examination, and their brain MRI images were segmented by the FBFCM algorithm. The MRI images before and after treatment were analyzed to evaluate the therapeutic effects of Mental Imagery on patients with motor and nerve dysfunction after stroke. The results showed that the segmentation coefficient of the FBFCM algorithm was 0.9315 and the segmentation entropy was 0.1098, which were significantly different from those of the traditional fuzzy c-means (FCM) algorithm. ( P &lt; 0.05 ), suggesting that the FBFCM algorithm had good segmentation effects on brain MRI images of stroke patients. After Mental Imagery, it was found that the patient’s Function Independent Measure (FIM) score was 99.04 ± 8.19, the Modified Barthel Index (MBI) score was 51.29 ± 4.35, the Fugl-Meyer (FMA) score was 61.01 ± 4.16, the neurological deficit degree in stroke (NFDS) score was 11.48 ± 2.01, the NIH Stroke Scale (NIHSS) score was 10.36 ± 1.69, and the clinical effective rate was 87.1%, all significantly different from those of the conventional rehabilitation training group ( P &lt; 0.05 ). Additionally, the brain area activated by Mental Imagery was more extensive. In conclusion, the FBFCM algorithm demonstrates superb capabilities in segmenting MRI images of stroke patients and is worth promotion in clinic. Mental Imagery can promote the neurological rehabilitation of patients by activating relevant brain areas of patients.

The gradual subjective consciousness fluctuation in implicit sequence learning and its relevant brain activity

Existing studies have investigated gradual subjective consciousnesses, guess, intuition, fluency, rule, and memory, and their fluctuation behavioral characteristics in implicit learning, but they did not investigate or elucidate the underlying brain mechanisms. Therefore, the current study asked participants to report subjective consciousnesses in each trial of inclusion and exclusion tasks after implicit sequence learning and used the eyes-closed and eyes-opened resting-states’ fMRI to examine the relevant brain areas of the five gradual subjective consciousnesses and their fluctuation. The results showed that: (1) There were many relevant resting-state brain areas of the five gradual subjective consciousnesses to reveal their brain mechanisms. In the eyes-closed and eyes-opened resting states, as the participants' consciousness level was gradually increasing from guess to intuition, to fluency, to rule, and to memory, the positively-relevant brain areas correspondingly changed from somatic motor to a mixture of somatic motor, consciousness, emotion feeling, and implicit learning; and then to a mixture of visual, somatic motor, and consciousness; and then to a mixture of visual, somatic motor, and consciousness; and then to a mixture of visual, somatic motor, and consciousness. The negatively-relevant brain areas correspondingly changed from a mixture of visual, consciousness, somatic sensory, and implicit learning to a mixture of visual, somatic motor, somatic sensory, and other consciousness; and then to memory; and then to a mixture of other somatic motors; and then to a mixture of other consciousness and other somatic motors. However, in the amplitude of low frequency fluctuations (ALFFs)-difference, the relative directions of the guess and intuition were almost opposite to those in the eyes-closed and eyes-opened resting states. But the relative directions of the fluency, rule, and memory were consistent with those in the eyes-closed and eyes-opened resting states. (2) There were significant gradual subjective consciousness fluctuations, including the gradual subjective consciousness fluctuation-all M and SD. There were many relevant resting-state brain areas of gradual subjective consciousness fluctuations to reveal their brain mechanisms. The gradual subjective consciousness fluctuation M was positively related to Calcarine_R, Lingual_R, Lingual_R, Temporal_Pole_Mid_L, ParaHippocampal_L, Vermis_1_2, and Vermis_7; but was negatively related to Calcarine_R. The gradual subjective consciousness fluctuation-all SD was positively related to Parietal_Inf_L, Thalamus_L, Temporal_Mid_L, Vermis_9, Parietal_Inf_L, and Thalamus_L and Thalamus_R; but was negatively related to Rolandic_Oper_R, Rolandic_Oper_R, Insula_L, Insula_R, Cingulum_Post_L, and Temporal_Mid_L. The detailed function of the relevant brain areas of consciousness fluctuations needs further investigation. (3) ALFFs in eyes-closed and eyes-opened resting states and their ALFFs-difference could differently predict the five gradual subjective consciousnesses and their fluctuations, indicating that using the two resting states was necessary, and the ALFFs-difference was a new quantitative sensitivity index of the gradual subjective consciousnesses and their fluctuations.

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics.

The nature of firefighters’ duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of and an accuracy of if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of and accuracy of when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

Association of Brain Reward Response With Body Mass Index and Ventral Striatal-Hypothalamic Circuitry Among Young Women With Eating Disorders

Eating disorders are severe psychiatric disorders; however, disease models that cross subtypes and integrate behavior and neurobiologic factors are lacking. To assess brain response during unexpected receipt or omission of a salient sweet stimulus across a large sample of individuals with eating disorders and healthy controls and test for evidence of whether this brain response is associated with the ventral striatal-hypothalamic circuitry, which has been associated with food intake control, and whether salient stimulus response and eating disorder related behaviors are associated. In this cross-sectional functional brain imaging study, young adults across the eating disorder spectrum were matched with healthy controls at a university brain imaging facility and eating disorder treatment program. During a sucrose taste classic conditioning paradigm, violations of learned associations between conditioned visual and unconditioned taste stimuli evoked the dopamine-related prediction error. Dynamic effective connectivity during expected sweet taste receipt was studied to investigate hierarchical brain activation between food intake relevant brain regions. The study was conducted from June 2014 to November 2019. Data were analyzed from December 2019 to February 2020. Prediction error brain reward response across insula and striatum; dynamic effective connectivity between hypothalamus and ventral striatum; and demographic and behavior variables and their correlations with prediction error brain response and connectivity edge coefficients. Of 317 female participants (197 with eating disorders and 120 healthy controls), the mean (SD) age was 23.8 (5.6) years and mean (SD) body mass index was 20.8 (5.4). Prediction error response was elevated in participants with anorexia nervosa (Wilks λ, 0.843; P = .001) and in participants with eating disorders inversely correlated with body mass index (left nucleus accumbens: r = -0.291; 95% CI, -0.413 to -0.167; P < .001; right dorsal anterior insula: r = -0.228; 95% CI, -0.366 to -0.089; P = .001), eating disorder inventory-3 binge eating tendency (left nucleus accumbens: r = -0.207; 95% CI, -0.333 to -0.073; P = .004; right dorsal anterior insula: r = -0.220; 95% CI, -0.354 to -0.073; P = .002), and trait anxiety (left nucleus accumbens: r = -0.148; 95% CI, -0.288 to -0.003; P = .04; right dorsal anterior insula: r = -0.221; 95% CI, -0.357 to -0.076; P = .002). Ventral striatal to hypothalamus directed connectivity was positively correlated with ventral striatal prediction error in eating disorders (r = 0.189; 95% CI, 0.045-0.324; P = .01) and negatively correlated with feeling out of control after eating (right side: r = -0.328; 95% CI, -0.480 to -0.164; P < .001; left side: r = -0.297; 95% CI, -0.439 to -0.142; P = .001). The results of this cross-sectional imaging study support that body mass index modulates prediction error and food intake control circuitry in the brain. Once altered, this circuitry may reinforce eating disorder behaviors when paired with behavioral traits associated with overeating or undereating.

The ecological validity of MET was favourable in sitting implicit sequence learning consciousness by eyes closed and eyes open resting states fMRI

The current study made participants sit to complete both the implicit sequence learning and the inclusion/exclusion tasks with the latter just after the former, and used eyes-closed and eyes-open resting states fMRI and their difference to test the ecological validity of the mutually exclusive theory (MET) in implicit-sequence-learning consciousness. (1) The behavioral and neuroimaging data did not support the process dissociation procedure, but did fit well with the MET. The correct inclusion-task response and the incorrect exclusion-task response were mutually exclusive with each other. The relevant brain areas of the two responses were either different or opposite in the eyes-closed and eyes-open resting-states and their difference. (2) ALFFs in eyes-closed and eyes-open resting-states and their difference were diversely related to the four MET knowledge in implicit sequence learning. The relevant brain areas of the four MET knowledge in the eyes-closed and eyes-open resting-state were the cerebral cortex responsible for vision, attention, cognitive control and consciousness, which could be called the upper consciousness network, and there were more relevant brain areas in the eyes-open resting-state than in the eye-closed resting-state.The relevant brain areas in ALFFs-difference were the subcortical nucleus responsible for sensory awareness, memory and implicit sequence learning, which could be called the lower consciousness network. ALFFs-difference could predict the four MET knowledge as a quantitative transition sensitivity index from internal feeling to external stimulus. (3) The relevant resting-state brain areas of the four MET knowledge were either different (for most brain areas, if some brain areas were related to one MET knowledge, they were not related to the other three MET knowledge) or opposite (for some brain areas, if some brain areas were positively related to one MET knowledge, they were negatively related to other MET knowledge). With the participants' control/consciousness level increasing from no-acquisition to controllable knowledge step by step, the positively relevant resting-state brain areas of the four MET knowledge changed from some consciousness network and the motor network, to some consciousness network and the implicit learning network, and then to some consciousness network; and the negatively relevant resting-state brain areas of the four MET knowledge changed from some consciousness network and visual perception network, to some consciousness network, then to some consciousness network and the motor network, and then to some consciousness network, the implicit learning network, and the motor network. In conclusion, the current study found the ecological validity of the MET was good in sitting posture and eyes-closed and eyes-open resting-states, ALFFs in eyes-closed and eyes-open resting-states and their difference could predict the four MET knowledge diversely, and the four MET knowledge had different or opposite relevant resting-state brain areas.

Correlated Evolution of Acrobatic Display and Both Neural and Somatic Phenotypic Traits in Manakins (Pipridae).

Brightly colored manakin (Aves: Pipridae) males are known for performing acrobatic displays punctuated by non-vocal sounds (sonations) in order to attract dull colored females. The complexity of the display sequence and assortment of display elements involved (e.g., sonations, acrobatic maneuvers, and cooperative performances) varies considerably across manakin species. Species-specific display elements coevolve with display-distinct specializations of the neuroanatomical, muscular, endocrine, cardiovascular, and skeletal systems in the handful of species studied. Conducting a broader comparative study, we previously found positive associations between display complexity and both brain mass and body mass across eight manakin genera, indicating selection for neural and somatic expansion to accommodate display elaboration. Whether this gross morphological variation is due to overall brain and body mass expansion (concerted evolution) versus size increases in only functionally relevant brain regions and growth of particular body ("somatic") features (mosaic evolution) remains to be explored. Here, we test the hypothesis that cross-species variation in male brain mass and body mass is driven by mosaic evolution. We predicted positive associations between display complexity and variation in the volume of the cerebellum and sensorimotor arcopallium, brain regions which have roles in sensorimotor processes, and learning and performance of precisely timed and sequenced thoughts and movements, respectively. In contrast, we predicted no associations between the volume of a limbic arcopallial nucleus or a visual thalamic nucleus and display complexity as these regions have no-specific functional relationship to display behavior. For somatic features, we predicted that the relationship between body mass and complexity would not include contributions of tarsus length based on a recent study suggesting selection on tarsus length is less labile than body mass. We tested our hypotheses in males from 12 manakin species and a closely related flycatcher. Our analyses support mosaic evolution of neural and somatic features functionally relevant to display and indicate that sexual selection for acrobatic complexity increases the capacity for procedural learning via cerebellar enlargement and may decrease maneuverability via increases in tarsus length.

Developing automated methods for disease subtyping in UK Biobank: an exemplar study on stroke

BackgroundBetter phenotyping of routinely collected coded data would be useful for research and health improvement. For example, the precision of coded data for hemorrhagic stroke (intracerebral hemorrhage [ICH] and subarachnoid hemorrhage [SAH]) may be as poor as < 50%. This work aimed to investigate the feasibility and added value of automated methods applied to clinical radiology reports to improve stroke subtyping.MethodsFrom a sub-population of 17,249 Scottish UK Biobank participants, we ascertained those with an incident stroke code in hospital, death record or primary care administrative data by September 2015, and ≥ 1 clinical brain scan report. We used a combination of natural language processing and clinical knowledge inference on brain scan reports to assign a stroke subtype (ischemic vs ICH vs SAH) for each participant and assessed performance by precision and recall at entity and patient levels.ResultsOf 225 participants with an incident stroke code, 207 had a relevant brain scan report and were included in this study. Entity level precision and recall ranged from 78 to 100%. Automated methods showed precision and recall at patient level that were very good for ICH (both 89%), good for SAH (both 82%), but, as expected, lower for ischemic stroke (73%, and 64%, respectively), suggesting coded data remains the preferred method for identifying the latter stroke subtype.ConclusionsOur automated method applied to radiology reports provides a feasible, scalable and accurate solution to improve disease subtyping when used in conjunction with administrative coded health data. Future research should validate these findings in a different population setting.

A Dual Mind Approach to Understanding the Conscious Self and Its Treatment

In this paper I will address questions about will, agency, choice, consciousness, relevant brain regions, impacts of disorders, and their therapeutics, and I will do this by referring to my theory, Dual-brain Psychology, which posits that within most of us there exist two mental agencies with different experiences, wills, choices, and behaviors. Each of these agencies is associated as a trait with one brain hemisphere (either left or right) and its composite regions. One of these agencies is more adversely affected by past traumas, and is more immature and more symptomatic, while the other is more mature and healthier. The theory has extensive experimental support through 17 peer-reviewed publications with clinical and non-clinical research. I will discuss how this theory relates to the questions about the nature of agency and I will also discuss my published theory on the physical nature of subjective experience and its relation to the brain, and how that theory interacts with Dual-Brain Psychology, leading to further insights into our human nature and its betterment.

Orientation of Temporal Interference for Non-invasive Deep Brain Stimulation in Epilepsy.

In patients with focal drug-resistant epilepsy, electrical stimulation from intracranial electrodes is frequently used for the localization of seizure onset zones and related pathological networks. The ability of electrically stimulated tissue to generate beta and gamma range oscillations, called rapid-discharges, is a frequent indication of an epileptogenic zone. However, a limit of intracranial stimulation is the fixed physical location and number of implanted electrodes, leaving numerous clinically and functionally relevant brain regions unexplored. Here, we demonstrate an alternative technique relying exclusively on non-penetrating surface electrodes, namely an orientation-tunable form of temporally interfering (TI) electric fields to target the CA3 of the mouse hippocampus which focally evokes seizure-like events (SLEs) having the characteristic frequencies of rapid-discharges, but without the necessity of the implanted electrodes. The orientation of the topical electrodes with respect to the orientation of the hippocampus is demonstrated to strongly control the threshold for evoking SLEs. Additionally, we demonstrate the use of Pulse-width-modulation of square waves as an alternative to sine waves for TI stimulation. An orientation-dependent analysis of classic implanted electrodes to evoke SLEs in the hippocampus is subsequently utilized to support the results of the minimally invasive temporally interfering fields. The principles of orientation-tunable TI stimulation seen here can be generally applicable in a wide range of other excitable tissues and brain regions, overcoming several limitations of fixed electrodes which penetrate tissue and overcoming several limitations of other non-invasive stimulation methods in epilepsy, such as transcranial magnetic stimulation (TMS).

A Task Agnostic Mental Fatigue Assessment Approach Based on EEG Frequency Bands for Demanding Maritime Operation

While it is known that one of the major causes of accidents in the maritime domain is excessive mental fatigue, objectively assessing mental fatigue in real-time in maritime operations remains a challenging and unanswered question. In this work, we aimed to develop an approach to assess mental fatigue in maritime operators in real-time and independent of the performed task. The method involves a simple setup comprised of a wireless electroencephalogram device, which provides relevant brain activity data to a mental fatigue assessment algorithm. The proposed algorithm uses normalized electroencephalogram energy information to monitor the development of mental fatigue in maritime operators. We tested our system in a realistic vessel simulator, and the results showed that it can detect the increase of mental fatigue levels.

Selecting the Most Relevant Brain Regions to Classify Children with Developmental Dyslexia and Typical Readers by Using Complex Magnocellular Stimuli and Multiple Kernel Learning.

Increasing evidence supports the presence of deficits in the visual magnocellular (M) system in developmental dyslexia (DD). The M system is related to the fronto-parietal attentional network. Previous neuroimaging studies have revealed reduced/absent activation within the visual M pathway in DD, but they have failed to characterize the extensive brain network activated by M stimuli. We performed a multivariate pattern analysis on a Region of Interest (ROI) level to differentiate between children with DD and age-matched typical readers (TRs) by combining full-field sinusoidal gratings, controlled for spatial and temporal frequencies and luminance contrast, and a coherent motion (CM) sensitivity task at 6%-CML6, 15%-CML15 and 40%-CML40. ROIs spanning the entire visual dorsal stream and ventral attention network (VAN) had higher discriminative weights and showed higher act1ivation in TRs than in children with DD. Of the two tasks, CM had the greatest weight when classifying TRs and children with DD in most of the ROIs spanning these streams. For the CML6, activation within the right superior parietal cortex positively correlated with reading skills. Our approach highlighted the dorsal stream and the VAN as highly discriminative areas between children with DD and TRs and allowed for a better characterization of the “dorsal stream vulnerability” underlying DD.

Consciousness, decision making, and volition: freedom beyond chance and necessity

What is the role of consciousness in volition and decision-making? Are our actions fully determined by brain activity preceding our decisions to act, or can consciousness instead affect the brain activity leading to action? This has been much debated in philosophy, but also in science since the famous experiments by Libet in the 1980s, where the current most common interpretation is that conscious free will is an illusion. It seems that the brain knows, up to several seconds in advance what “you” decide to do. These studies have, however, been criticized, and alternative interpretations of the experiments can be given, some of which are discussed in this paper. In an attempt to elucidate the processes involved in decision-making (DM), as an essential part of volition, we have developed a computational model of relevant brain structures and their neurodynamics. While DM is a complex process, we have particularly focused on the amygdala and orbitofrontal cortex (OFC) for its emotional, and the lateral prefrontal cortex (LPFC) for its cognitive aspects. In this paper, we present a stochastic population model representing the neural information processing of DM. Simulation results seem to confirm the notion that if decisions have to be made fast, emotional processes and aspects dominate, while rational processes are more time consuming and may result in a delayed decision. Finally, some limitations of current science and computational modeling will be discussed, hinting at a future development of science, where consciousness and free will may add to chance and necessity as explanation for what happens in the world.

Endogenous memory reactivation during sleep in humans is clocked by slow oscillation-spindle complexes

Sleep is thought to support memory consolidation via reactivation of prior experiences, with particular electrophysiological sleep signatures (slow oscillations (SOs) and sleep spindles) gating the information flow between relevant brain areas. However, empirical evidence for a role of endogenous memory reactivation (i.e., without experimentally delivered memory cues) for consolidation in humans is lacking. Here, we devised a paradigm in which participants acquired associative memories before taking a nap. Multivariate decoding was then used to capture endogenous memory reactivation during non-rapid eye movement (NREM) sleep in surface EEG recordings. Our results reveal reactivation of learning material during SO-spindle complexes, with the precision of SO-spindle coupling predicting reactivation strength. Critically, reactivation strength (i.e. classifier evidence in favor of the previously studied stimulus category) in turn predicts the level of consolidation across participants. These results elucidate the memory function of sleep in humans and emphasize the importance of SOs and spindles in clocking endogenous consolidation processes.

Threat imminence modulates neural gain in attention and motor relevant brain circuits in humans.

Different levels of threat imminence elicit distinct computational strategies reflecting how the organism interacts with its environment in order to guarantee survival. Thereby, parasympathetically driven orienting and inhibition of on-going behavior in post-encounter situations and defense reactions in circa-strike conditions associated with sympathetically driven action preparation are typically observed across species. Here, we show that healthy humans are characterized by markedly variable individual orienting or defense response tendencies as indexed by differential heart rate (HR) changes during the passive viewing of unpleasant pictures. Critically, these HR response tendencies predict neural gain modulations in cortical attention and preparatory motor circuits as measured by neuromagnetic steady-state visual evoked fields (ssVEFs) and induced beta-band (19-30Hz) desynchronization, respectively. Decelerative HR orienting responses were associated with increased ssVEF power in the parietal cortex and reduced beta-band desynchronization in pre-motor and motor areas. However, accelerative HR defense response tendencies covaried with reduced ssVEF power in the parietal cortex and lower beta-band desynchronization in cortical motor circuits. These results show that neural gain in attention- and motor-relevant brain areas is modulated by HR indexed threat imminence during the passive viewing of unpleasant pictures. The observed mutual ssVEF and beta-band power modulations in attention and motor brain circuits support the idea of two prevalent response tendencies characterized by orienting and motor inhibition or reduced stimulus processing and action initiation tendencies at different perceived threat imminence levels.

Retinal Ganglion Cells-Diversity of Cell Types and Clinical Relevance.

Retinal ganglion cells (RGCs) are the bridging neurons that connect the retinal input to the visual processing centres within the central nervous system. There is a remarkable diversity of RGCs and the various subtypes have unique morphological features, distinct functions, and characteristic pathways linking the inner retina to the relevant brain areas. A number of psychophysical and electrophysiological tests have been refined to investigate this large and varied population of RGCs. Technological advances, such as high-resolution optical coherence tomography imaging, have provided additional tools to define the pattern of RGC involvement and the chronological sequence of events in both inherited and acquired optic neuropathies. The mechanistic insights gained from these studies, in particular the selective vulnerability and relative resilience of particular RGC subtypes, are of fundamental importance as they are directly relevant to the development of targeted therapies for these invariably progressive blinding diseases. This review provides a comprehensive description of the various types of RGCs, the developments in proposed methods of classification, and the current gaps in our knowledge of how these RGCs are differentially affected depending on the underlying aetiology. The synthesis of the current body of knowledge on the diversity of RGCs and the pathways that are potentially amenable to therapeutic modulation will hopefully lead to much needed effective treatments for patients with optic neuropathies.

Inter-Cohort Validation of SuStaIn Model for Alzheimer's Disease.

Alzheimer’s disease (AD) is a neurodegenerative disorder which spans several years from preclinical manifestations to dementia. In recent years, interest in the application of machine learning (ML) algorithms to personalized medicine has grown considerably, and a major challenge that such models face is the transferability from the research settings to clinical practice. The objective of this work was to demonstrate the transferability of the Subtype and Stage Inference (SuStaIn) model from well-characterized research data set, employed as training set, to independent less-structured and heterogeneous test sets representative of the clinical setting. The training set was composed of MRI data of 1043 subjects from the Alzheimer’s disease Neuroimaging Initiative (ADNI), and the test set was composed of data from 767 subjects from OASIS, Pharma-Cog, and ViTA clinical datasets. Both sets included subjects covering the entire spectrum of AD, and for both sets volumes of relevant brain regions were derived from T1-3D MRI scans processed with Freesurfer v5.3 cross-sectional stream. In order to assess the predictive value of the model, subpopulations of subjects with stable mild cognitive impairment (MCI) and MCIs that progressed to AD dementia (pMCI) were identified in both sets. SuStaIn identified three disease subtypes, of which the most prevalent corresponded to the typical atrophy pattern of AD. The other SuStaIn subtypes exhibited similarities with the previously defined hippocampal sparing and limbic predominant atrophy patterns of AD. Subject subtyping proved to be consistent in time for all cohorts and the staging provided by the model was correlated with cognitive performance. Classification of subjects on the basis of a combination of SuStaIn subtype and stage, mini mental state examination and amyloid-β1-42 cerebrospinal fluid concentration was proven to predict conversion from MCI to AD dementia on par with other novel statistical algorithms, with ROC curves that were not statistically different for the training and test sets and with area under curve respectively equal to 0.77 and 0.76. This study proves the transferability of a SuStaIn model for AD from research data to less-structured clinical cohorts, and indicates transferability to the clinical setting.

Social brain networks: Resting-state and task-based connectivity in youth with and without epilepsy

Individuals with epilepsy often experience social difficulties and deficits in social cognition. It remains unknown how disruptions to neural networks underlying such skills may contribute to this clinical phenotype. The current study compared the organization of relevant brain circuits—the “mentalizing network” and a salience-related network centered on the amygdala—in youth with and without epilepsy. Functional connectivity between the nodes of these networks was assessed, both at rest and during engagement in a social cognitive task (facial emotion recognition), using functional magnetic resonance imaging. There were no group differences in resting-state connectivity within either neural network. In contrast, youth with epilepsy showed comparatively lower connectivity between the left posterior superior temporal sulcus and the medial prefrontal cortex—but greater connectivity within the left temporal lobe—when viewing faces in the task. These findings suggest that the organization of a mentalizing network underpinning social cognition may be disrupted in youth with epilepsy, though differences in connectivity within this circuit may shift depending on task demands. Our results highlight the importance of considering functional task-based engagement of neural systems in characterizations of network dysfunction in epilepsy.

Absence of Sex Differences Identified in Food Motivation Pathways in Youth with Avoidant/Restrictive Food Intake Disorder (ARFID)

Background: Avoidant/Restrictive Food Intake Disorder (ARFID) is a recent diagnosis incorporated into the DSM-5 to provide diagnostic specificity to individuals who may have avoidant/restrictive eating behavior unrelated to body image/weight concerns and display three core profiles: insufficient intake/low interest in feeding, fear of aversive consequences related to food intake (e.g., choking) and avoidance based on sensory characteristics of food. Various studies have shown a higher preponderance of male patients in ARFID compared to other eating disorder groups. To elucidate potential sex differences in the neurobiology of ARFID, we examined food motivation pathways by assessing levels of key appetite regulating hormones, anorexigenic peptide YY (PYY) and orexigenic ghrelin, and fMRI activation of relevant brain circuitry in females compared to males with ARFID. Based on prior fMRI studies in healthy controls, we hypothesized that in a fasted state, females (vs. males) would demonstrate greater blood-oxygen-level-dependent (BOLD) activation in response to high-calorie food (vs. non-food) images in the orbitofrontal cortex (OFC) and the right lateral prefrontal cortex (LPFC), while males (vs. females) would demonstrate greater activation in the right hippocampus.Methods: Seventy-five adolescents and young adults with ARFID and sub-threshold ARFID (43 female) were studied after a 10-hour overnight fast. PYY and ghrelin levels were assessed in a subset of 62 individuals (31 female). All participants completed fMRI imaging in a 3T scanner while viewing images of foods, non-food items, and fixation stimuli. Functional MRI data were analyzed using SPM12. A priori regions of interest included the right lateral prefrontal cortex (LPFC), right hippocampus and orbitofrontal cortex (OFC). Secondary exploratory whole-brain analysis was also performed. Statistical significance is reported at the p<0.05 level.Results: Females and males did not differ in age ((mean±SD): 16.1±3.7 years) or BMI (19.9±5.4 kg/m2). There were no statistically significant differences between females and males in PYY (p=0.10) or ghrelin (p=0.47) levels. Furthermore, analysis of fMRI data yielded no significant differences between females and males with ARFID in a priori regions of interest (OFC: p(FWE-corr)>0.50; R LPFC: no suprathreshold clusters, or R hippocampus: p(FWE-corr)=0.25 and 0.33) or in the secondary whole-brain analysis (cluster p(FWE-corr)=0.304).Conclusion: This is the first study to investigate sex differences in the neurobiology of ARFID, an important line of research to advance treatment approaches. We found no sex-specific neurobiological differences in adolescents and young adults with ARFID. Future studies with larger sample sizes are needed to further investigate potential sex differences across the different ARFID profiles.

Oxytocin receptor is a potential biomarker of the hyporesponsive HPA axis subtype of PTSD and might be modulated by HPA axis reactivity traits in humans and mice

This study aimed to identify yet unavailable blood biomarkers for the responsive and the hyporesponsive hypothalamic-pituitary-adrenal (HPA) axis subtypes of posttraumatic stress disorder (PTSD). As, I, we recently discovered the intranasal neuropeptide oxytocin to reduce experimentally provoked PTSD symptoms, II, expression of its receptor (OXTR) has hitherto not been assessed in PTSD patients, and III, oxytocin and OXTR have previously been related to the HPA axis, we considered both as suitable candidates. During a Trier Social Stress Test (TSST), we compared serum oxytocin and blood OXTR mRNA concentrations between female PTSD patients, their HPA axis reactivity subtypes and sex and age-matched healthy controls (HC). At baseline, both candidates differentiated the hyporesponsive HPA axis subtype from HC, however, only baseline OXTR mRNA discriminated also between subtypes. Furthermore, in the hyporesponsive HPA axis subgroup, OXTR mRNA levels correlated with PTSD symptoms and changed markedly during the TSST. To assess the influence of (traumatic) stress on the cerebral expression of oxytocin and its receptor and to test their suitability as biomarkers for the mouse PTSD-like syndrome, we then analyzed oxytocin, its mRNA (Oxt) and Oxtr mRNA in three relevant brain regions and Oxt in blood of a PTSD mouse model. To further explore the HPA axis reactivity subtype dependency of OXTR, we compared cerebral OXTR protein expression between mice exhibiting two different HPA axis reactivity traits, i.e., FK506 binding protein 51 knockout vs. wildtype mice. In summary, blood OXTR mRNA emerged as a potential biomarker of the hyporesponsive HPA axis PTSD subtype and prefrontal cortical Oxtr and Oxt of the mouse PTSD-like syndrome. Moreover, we found first translational evidence for a HPA axis responsivity trait-dependent regulation of OXTR expression. The lack of a cohort of the (relatively rare) hyporesponsive HPA axis subtype of HC is a limitation of our study.