Body mass, neuro-hormonal stress processing, and disease activity in lean to obese people with multiple sclerosis.

Overweight and obesity can worsen disease activity in multiple sclerosis (MS). Although psychobiological stress processing is increasingly recognized as important obesity factor that is tightly connected to proinflammatory metabolic hormones and cytokines, its role for MS obesity remains unexplored. Consequently, we investigated the interplay between body mass index (BMI), neural stress processing (functional connectivity, FC), and immuno-hormonal stress parameters (salivary cortisol and T cell glucocorticoid [GC] sensitivity) in 57 people with MS (six obese, 19 over-, 28 normal-, and four underweight; 37 females, 46.4 ± 10.6 years) using an Arterial-Spin-Labeling MRI task comprising a rest and stress stage, along with quantitative PCR. Our findings revealed significant positive connections between BMI and MS disease activity (i.e., higher BMI was accompanied by higher relapse rate). BMI was positively linked to right supramarginal gyrus and anterior insula FC during rest and negatively to right superior parietal lobule and cerebellum FC during stress. BMI showed associations with GC functioning, with higher BMI associated with lower CD8+ FKBP4 expression and higher CD8+ FKBP5 expression on T cells. Finally, the expression of CD8+ FKBP4 positively correlated with the FC of right supramarginal gyrus and left superior parietal lobule during rest. Overall, our study provides evidence that body mass is tied to neuro-hormonal stress processing in people with MS. The observed pattern of associations between BMI, neural networks, and GC functioning suggests partial overlap between neuro-hormonal and neural-body mass networks. Ultimately, the study underscores the clinical importance of understanding multi-system crosstalk in MS obesity.

Similar neural pathways link psychological stress and brain-age in health and multiple sclerosis.

Clinical and neuroscientific studies suggest a link between psychological stress and reduced brain health in health and neurological disease but it is unclear whether mediating pathways are similar. Consequently, we applied an arterial-spin-labeling MRI stress task in 42 healthy persons and 56 with multiple sclerosis, and investigated regional neural stress responses, associations between functional connectivity of stress-responsive regions and the brain-age prediction error, a highly sensitive machine learning brain health biomarker, and regional brain-age constituents in both groups. Stress responsivity did not differ between groups. Although elevated brain-age prediction errors indicated worse brain health in patients, anterior insula-occipital cortex (healthy persons: occipital pole; patients: fusiform gyrus) functional connectivity correlated with brain-age prediction errors in both groups. Finally, also gray matter contributed similarly to regional brain-age across groups. These findings might suggest a common stress-brain health pathway whose impact is amplified in multiple sclerosis by disease-specific vulnerability factors.

Prefrontal-amygdala emotion regulation and depression in multiple sclerosis.

Depression is among the most common comorbidities in multiple sclerosis and has severe psychosocial consequences. Alterations in neural emotion regulation in amygdala and prefrontal cortex have been recognized as key mechanism of depression but never been investigated in multiple sclerosis depression. In this cross-sectional observational study, we employed a functional MRI task investigating neural emotion regulation by contrasting regulated versus unregulated negative stimulus perception in 16 persons with multiple sclerosis and depression (47.9 ± 11.8 years; 14 female) and 26 persons with multiple sclerosis but without depression (47.3 ± 11.7 years; 14 female). We tested the impact of depression and its interaction with lesions in amygdala-prefrontal fibre tracts on brain activity reflecting emotion regulation. A potential impact of sex, age, information processing speed, disease duration, overall lesion load, grey matter fraction, and treatment was taken into account in these analyses. Patients with depression were less able (i) to downregulate negative emotions than those without (t = -2.25, P = 0.012, ß = -0.33) on a behavioural level according to self-report data and (ii) to downregulate activity in a left amygdala coordinate (t = 3.03, P Family-wise error [FWE]-corrected = 0.017, ß = 0.39). Moreover, (iii) an interdependent effect of depression and lesions in amygdala-prefrontal tracts on activity was found in two left amygdala coordinates (t = 3.53, pFWE = 0.007, ß = 0.48; t = 3.21, pFWE = 0.0158, ß = 0.49) and one right amygdala coordinate (t = 3.41, pFWE = 0.009, ß = 0.51). Compatible with key elements of the cognitive depression theory formulated for idiopathic depression, our study demonstrates that depression in multiple sclerosis is characterized by impaired neurobehavioural emotion regulation. Complementing these findings, it shows that the relation between neural emotion regulation and depression is affected by lesion load, a key pathological feature of multiple sclerosis, located in amygdala-prefrontal tracts.

Central stress processing, T-cell responsivity to stress hormones and disease severity in multiple sclerosis.

Epidemiological, clinical and neuroscientific studies support a link between psychobiological stress and multiple sclerosis. Neuroimaging suggests that blunted central stress processing goes along with higher multiple sclerosis severity, neuroendocrine studies suggest that blunted immune system sensitivity to stress hormones is linked to stronger neuroinflammation. Until now, however, no effort has been made to elucidate whether central stress processing and immune system sensitivity to stress hormones are related in a disease-specific fashion, and if so, whether this relation is clinically meaningful. Consequently, we conducted two functional MRI analyses based on a total of 39 persons with multiple sclerosis and 25 healthy persons. Motivated by findings of an altered interplay between neuroendocrine stress processing and T-cell glucocorticoid sensitivity in multiple sclerosis, we searched for neural networks whose stress task-evoked activity is differentially linked to peripheral T-cell glucocorticoid signalling in patients versus healthy persons as a potential indicator of disease-specific CNS-immune crosstalk. Subsequently, we tested whether this activity is simultaneously related to disease severity. We found that activity of a network comprising right anterior insula, right fusiform gyrus, left midcingulate and lingual gyrus was differentially coupled to T-cell glucocorticoid signalling across groups. This network's activity was simultaneously linked to patients' lesion volume, clinical disability and information-processing speed. Complementary analyses revealed that T-cell glucocorticoid signalling was not directly linked to disease severity. Our findings show that alterations in the coupling between central stress processing and T-cell stress hormone sensitivity are related to key severity measures of multiple sclerosis.

Immune signature of multiple sclerosis-associated depression.

Multiple neurobiological pathways have been implicated in the pathobiology of major depressive disorder (MDD). The identification of reliable biological substrates across the entire MDD spectrum, however, is hampered by a vast heterogeneity in the clinical presentation, presumably as a consequence of heterogeneous pathobiology. One way to overcome this limitation could be to explore disease subtypes based on biological similarity such as "inflammatory depression". As such a subtype may be particularly enriched in depressed patients with an underlying inflammatory condition, multiple sclerosis (MS) could provide an informative disease context for this approach. Few studies have explored immune markers of MS-associated depression and replications are missing. To address this, we analyzed data from two independent case-control studies on immune signatures of MS-associated depression, conducted at two different academic MS centers (overall sample size of n = 132). Using a stepwise data-driven approach, we identified CD4+CCR7lowTCM cell frequencies as a robust correlate of depression in MS. This signature was associated with core symptoms of depression and depression severity (but not MS severity per se) and linked to neuroinflammation as determined by magnetic resonance imaging (MRI). Furthermore, exploratory analyses of T cell polarization revealed this was largely driven by cells with a TH1-like phenotype. Our findings suggest (neuro)immune pathways linked to affective symptoms of autoimmune disorders such as MS, with potential relevance for the understanding of "inflammatory" subtypes of depression.

Computational approaches to predicting treatment response to obesity using neuroimaging.

Obesity is a worldwide disease associated with multiple severe adverse consequences and comorbid conditions. While an increased body weight is the defining feature in obesity, etiologies, clinical phenotypes and treatment responses vary between patients. These variations can be observed within individual treatment options which comprise lifestyle interventions, pharmacological treatment, and bariatric surgery. Bariatric surgery can be regarded as the most effective treatment method. However, long-term weight regain is comparably frequent even for this treatment and its application is not without risk. A prognostic tool that would help predict the effectivity of the individual treatment methods in the long term would be essential in a personalized medicine approach. In line with this objective, an increasing number of studies have combined neuroimaging and computational modeling to predict treatment outcome in obesity. In our review, we begin by outlining the central nervous mechanisms measured with neuroimaging in these studies. The mechanisms are primarily related to reward-processing and include "incentive salience" and psychobehavioral control. We then present the diverse neuroimaging methods and computational prediction techniques applied. The studies included in this review provide consistent support for the importance of incentive salience and psychobehavioral control for treatment outcome in obesity. Nevertheless, further studies comprising larger sample sizes and rigorous validation processes are necessary to answer the question of whether or not the approach is sufficiently accurate for clinical real-world application.

Neural Processes of Psychological Stress and Relaxation Predict the Future Evolution of Quality of Life in Multiple Sclerosis.

Health-related quality of life (HRQoL) is an essential complementary parameter in the assessment of disease burden and treatment outcome in multiple sclerosis (MS) and can be affected by neuropsychiatric symptoms, which in turn are sensitive to psychological stress. However, until now, the impact of neurobiological stress and relaxation on HRQoL in MS has not been investigated. We thus evaluated whether the activity of neural networks triggered by mild psychological stress (elicited in an fMRI task comprising mental arithmetic with feedback) or by stress termination (i.e., relaxation) at baseline (T0) predicts HRQoL variations occurring between T0 and a follow-up visit (T1) in 28 patients using a robust regression and permutation testing. The median delay between T0 and T1 was 902 (range: 363-1,169) days. We assessed HRQoL based on the Hamburg Quality of Life Questionnaire in MS (HAQUAMS) and accounted for the impact of established HRQoL predictors and the cognitive performance of the participants. Relaxation-triggered activity of a widespread neural network predicted future variations in overall HRQoL (t = 3.68, p family-wise error [FWE]-corrected = 0.008). Complementary analyses showed that relaxation-triggered activity of the same network at baseline was associated with variations in the HAQUAMS mood subscale on an αFWE = 0.1 level (t = 3.37, p FWE = 0.087). Finally, stress-induced activity of a prefronto-limbic network predicted future variations in the HAQUAMS lower limb mobility subscale (t = -3.62, p FWE = 0.020). Functional neural network measures of psychological stress and relaxation contain prognostic information for future HRQoL evolution in MS independent of clinical predictors.

Patch individual filter layers in CNNs to harness the spatial homogeneity of neuroimaging data.

Convolutional neural networks (CNNs)-as a type of deep learning-have been specifically designed for highly heterogeneous data, such as natural images. Neuroimaging data, however, is comparably homogeneous due to (1) the uniform structure of the brain and (2) additional efforts to spatially normalize the data to a standard template using linear and non-linear transformations. To harness spatial homogeneity of neuroimaging data, we suggest here a new CNN architecture that combines the idea of hierarchical abstraction in CNNs with a prior on the spatial homogeneity of neuroimaging data. Whereas early layers are trained globally using standard convolutional layers, we introduce patch individual filters (PIF) for higher, more abstract layers. By learning filters in individual latent space patches without sharing weights, PIF layers can learn abstract features faster and specific to regions. We thoroughly evaluated PIF layers for three different tasks and data sets, namely sex classification on UK Biobank data, Alzheimer's disease detection on ADNI data and multiple sclerosis detection on private hospital data, and compared it with two baseline models, a standard CNN and a patch-based CNN. We obtained two main results: First, CNNs using PIF layers converge consistently faster, measured in run time in seconds and number of iterations than both baseline models. Second, both the standard CNN and the PIF model outperformed the patch-based CNN in terms of balanced accuracy and receiver operating characteristic area under the curve (ROC AUC) with a maximal balanced accuracy (ROC AUC) of 94.21% (99.10%) for the sex classification task (PIF model), and 81.24% and 80.48% (88.89% and 87.35%) respectively for the Alzheimer's disease and multiple sclerosis detection tasks (standard CNN model). In conclusion, we demonstrated that CNNs using PIF layers result in faster convergence while obtaining the same predictive performance as a standard CNN. To the best of our knowledge, this is the first study that introduces a prior in form of an inductive bias to harness spatial homogeneity of neuroimaging data.

Delayed access to conscious processing in multiple sclerosis: Reduced cortical activation and impaired structural connectivity.

Although multiple sclerosis (MS) is frequently accompanied by visuo-cognitive impairment, especially functional brain mechanisms underlying this impairment are still not well understood. Consequently, we used a functional MRI (fMRI) backward masking task to study visual information processing stratifying unconscious and conscious in MS. Specifically, 30 persons with MS (pwMS) and 34 healthy controls (HC) were shown target stimuli followed by a mask presented 8-150 ms later and had to compare the target to a reference stimulus. Retinal integrity (via optical coherence tomography), optic tract integrity (visual evoked potential; VEP) and whole brain structural connectivity (probabilistic tractography) were assessed as complementary structural brain integrity markers. On a psychophysical level, pwMS reached conscious access later than HC (50 vs. 16 ms, p < .001). The delay increased with disease duration (p < .001, ß = .37) and disability (p < .001, ß = .24), but did not correlate with conscious information processing speed (Symbol digit modality test, ß = .07, p = .817). No association was found for VEP and retinal integrity markers. Moreover, pwMS were characterized by decreased brain activation during unconscious processing compared with HC. No group differences were found during conscious processing. Finally, a complementary structural brain integrity analysis showed that a reduced fractional anisotropy in corpus callosum and an impaired connection between right insula and primary visual areas was related to delayed conscious access in pwMS. Our study revealed slowed conscious access to visual stimulus material in MS and a complex pattern of functional and structural alterations coupled to unconscious processing of/delayed conscious access to visual stimulus material in MS.

Blunted neural and psychological stress processing predicts future grey matter atrophy in multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is characterized by two neuropathological key aspects: inflammation and neurodegeneration. Clinical studies support a prospective link between psychological stress and subsequent inflammatory disease activity. However, it is unknown if a similar link exists for grey matter (GM) degeneration as the key driver of irreversible disability. METHODS: We tested whether neural network activity triggered in a psychological fMRI stress paradigm (a mental arithmetic task including social evaluation) conducted at a baseline time point predicts future GM atrophy in 25 persons with MS (14 females). Atrophy was determined between the baseline and a follow-up time point with a median delay of 1012 (Rg: 717-1439) days. Additionally, atrophy was assessed in 22 healthy subjects (13 females; median delay 771 [Rg: 740-908] days between baseline and follow-up) for comparison. RESULTS: An analysis of longitudinal atrophy in patients revealed GM loss in frontal, parietal, and cerebellar areas. Cerebellar atrophy was more pronounced in patients than controls. Future parietal and cerebellar atrophy could be predicted based on activity of two networks. Perceived psychological stress was negatively related to future parietal atrophy in patients and activity of the network predictive of parietal atrophy was positively linked to perceived stress. CONCLUSIONS: We have shown that blunted neural and psychological stress processing have a detrimental effect on the course of MS and are interrelated. Together with research showing that psychological and neural stress processing can be altered through interventions, our findings suggest that stress processing might constitute an important modifiable disease factor.

Altered Coupling of Psychological Relaxation and Regional Volume of Brain Reward Areas in Multiple Sclerosis.

Background: Psychological stress can influence the severity of multiple sclerosis (MS), but little is known about neurobiological factors potentially counteracting these effects. Objective: To identify gray matter (GM) brain regions related to relaxation after stress exposure in persons with MS (PwMS). Methods: 36 PwMS and 21 healthy controls (HCs) reported their feeling of relaxation during a mild stress task. These markers were related to regional GM volumes, heart rate, and depressive symptoms. Results: Relaxation was differentially linked to heart rate in both groups (t = 2.20, p = 0.017), i.e., both markers were only related in HCs. Relaxation was positively linked to depressive symptoms across all participants (t = 1.99, p = 0.045) although this link differed weakly between groups (t = 1.62, p = 0.108). Primarily, the volume in medial temporal gyrus was negatively linked to relaxation in PwMS (t = -5.55, pfamily-wise-error(FWE)corrected = 0.018). A group-specific coupling of relaxation and GM volume was found in ventromedial prefrontal cortex (VMPFC) (t = -4.89, pFWE = 0.039). Conclusion: PwMS appear unable to integrate peripheral stress signals into their perception of relaxation. Together with the group-specific coupling of relaxation and VMPFC volume, a key area of the brain reward system for valuation of affectively relevant stimuli, this finding suggests a clinically relevant misinterpretation of stress-related affective stimuli in MS.

Visual system damage and network maladaptation are associated with cognitive performance in neuromyelitis optica spectrum disorders.

Background Neuromyelitis Optica Spectrum Disorders (NMOSD) is an autoimmune disease leading to disability from optic neuritis, myelitis and more rarely brain stem attacks and encephalitis. Patients with NMOSD also exhibit cognitive deficits, the cause of which remains unclear. Recent evidence highlights sensory-cognitive parallel processing converging on the primary visual cortex. The objective of this study was to investigate the effect of the primary visual network disruption from damage caused by optic neuritis on cognition in NMOSD. Methods Twenty-nine aquaporin-4 antibody seropositive patients with NMOSD and 22 healthy controls (HC) completed the brief repeatable battery of neuropsychological tests (BRB-N) and underwent 3 Tesla MRI. Primary visual network functional connectivity (FC) at resting state was analyzed and correlated with performance on BRB-N. These correlations were compared between the groups. Results Patients performed significantly worse than HC on the BRB-N Index score (t = 2.366, p = 0.02). Among HC, visual network FC decreased significantly as cognitive performance on the BRB-N Index score increased (rho(17)=-0.507, p = 0.02). Among patients, this association was absent (rho(23)=0.197, p = 0.18), and the difference in correlation direction and strength to HC was significant (z=-2.175, p = 0.01). Visual network FC was able to explain 19% of the variance in cognitive performance in HC, but none in patients. Conclusions A physiological association of the primary visual network FC and cognitive performance appears absent in patients with NMOSD, suggesting a partial explanation for cognitive deficits. Our findings extend neuroscientific concepts on sensory-cognitive parallel processing neural networks to a clearly defined pathological state, and may be relevant for other diseases with visual system damage.

Association Between Fatigue and Motor Exertion in Patients With Multiple Sclerosis-a Prospective Study.

Background: Fatigue in multiple sclerosis (MS) is conceived as a multidimensional construct. Objectives: This study aims to describe the changes of balance and gait parameters after 6 min of walking (6 MW) as potential quantitative markers for perceptions of state fatigue and trait fatigue in MS. Methods: A total of 19 patients with MS (17 with fatigue) and 24 healthy subjects underwent static posturography, gait analysis, and ratings of perceived exertion before and after 6 MW. Results: 6 MW was perceived as exhaustive, but both groups featured more dynamic comfortable speed walking after 6 MW. Shorter stride length at maximum speed and increased postural sway after 6 MW indicated fatigability of balance and gait in MS group only. While most changes were related to higher levels of perceived exertion after 6 MW (state fatigue), higher fatigue ratings (trait fatigue) were only associated with less increase in arm swing at comfortable speed. Further analysis revealed different associations of trait fatigue and performance fatigability with disability and motor functions. Performance fatigability was most closely related to the Expanded Disability Status Scale, while for trait fatigue, the strongest correlations were seen with balance function and handgrip strength. Conclusions: Fatigability of performance was closely related to perceptions of exertion after 6 MW (state fatigue) and disability in MS but distinct from fatigue ratings, conceived as trait fatigue. Our study identified postural sway, arm swing during gait, and hand grip strength as unexpected potential motor indicators of fatigue ratings in MS.

Viscoelasticity of striatal brain areas reflects variations in body mass index of lean to overweight male adults.

Although a variety of MRI studies investigated the link between body mass index (BMI) and parameters of neural gray matter (GM), the technique applied in most of these studies, voxel-based morphometry (VBM), focusses on the regional GM volume, a macroscopic tissue property. Thus, the studies were not able to exploit the BMI-related information contained in the GM microstructure although PET studies suggest that these factors are important. Here, we used cerebral MR Elastography (MRE) to characterize features of tissue microstructure by evaluating the propagation of shear waves applied to the skull and to assess local tissue viscoelasticity to test the link between this parameter and BMI in 22 lean to overweight males. Unlike the majority of existing MRE studies investigating neural viscoelasticity signals averaged across large brain regions, we used the viscoelasticity of individual voxels for our experiment. Our technique revealed a negative link between BMI and viscoelasticity of two areas of the striatal reward system, i.e., right putamen (t = -8.2; pFWE-corrected = 0.005) and left globus pallidus (t = -7.1; pFWE = 0.037) which was independent of GM volume at these coordinates. Finally, comparison of BMI models based on individual voxels vs. on signals averaged across brain atlas regions demonstrates that voxel-based models explain a significantly higher proportion of variance. Consequently, our findings show that cerebral MRE is suitable to identify medically relevant microstructural tissue properties. Using a voxel-wise analysis approach, we were able to utilize the high spatial resolution of MRE for mapping BMI-related information in the brain.

Interaction of circulating GLP-1 and the response of the dorsolateral prefrontal cortex to food-cues predicts body weight development.

OBJECTIVES: This study evaluated the impact of the interaction between the anorexigenic incretin hormone glucagon-like peptide-1 (GLP-1) and reward-related brain activity in the dorsolateral prefrontal cortex (DLPFC), a key area of behavioral control, on future weight loss in obese individuals. METHODS: We performed a weight loss-weight maintenance intervention study over 27 months. We applied an fMRI food-cue reactivity paradigm during which the participants were passively exposed to food pictures to evaluate neuronal activity in the DLPFC. Additionally, we measured concentrations of circulating GLP-1 levels during a standard oral glucose tolerance test. Phenotyping was performed consecutively before and after a 3-month low-calorie diet as well as after a randomized 12-month trial, investigating the effect of a combined behavioral intervention on body weight maintenance. Participants were then followed-up for another 12 months without further intervention. RESULTS: Using voxel-wise linear mixed-effects regression analyses, we evaluated 56 measurements and identified a strong interaction between circulating, endogenous GLP-1 levels and DLPFC activity predicting body weight change over the total observation period (t = -6.17, p = 1.6 · 10-7). While neither the GLP-1 nor the DLPFC response individually predicted the subsequent weight change, participants achieved body weight loss when the GLP-1 and the DLPFC responses occurred concurrently. CONCLUSIONS: Our data demonstrate an interaction between a peripheral hormonal signal and central nervous activity as robust predictor of body weight change throughout the different periods of a long-term life-style intervention. The preeminent role of their interdependency compared to the partly ambivalent effects of the single components argues for integrative approaches to improve sensitivity and reliability of weight prediction conventionally based on individual biomarkers.

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation.

Machine learning-based imaging diagnostics has recently reached or even surpassed the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on 3D convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS), the most widespread autoimmune neuroinflammatory disease. MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients (n = 76) and healthy controls (n = 71). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of CNN models transparent, which could serve to justify classification decisions for clinical review, verify diagnosis-relevant features and potentially gather new disease knowledge.

Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer's Disease Classification.

Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent, making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance/relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation ("Which change in voxels would change the outcome most?"), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals ("Why does this person have AD?") with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g., relevance density or relevance gain. Although these metrics produce very individual "fingerprints" of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data.

No Sex Difference Found: Cues of Sexual Stimuli Activate the Reward System in both Sexes.

Sexually explicit material (SEM) is increasingly used in western societies. One reason for this high usage might be the rewarding property of SEM demonstrated in many brain imaging studies showing an activation of the reward system during the presentation of SEM. It is not yet well understood why women use SEM to a remarkably lesser extent than men. Maybe men react stronger to stimuli - so called SEM cues -, which signal the presentation of SEM and are therefore more vulnerable to use SEM than women. Therefore, the present study aimed at investigating the sex specific neural correlates towards SEM and SEM cues. We were further interested in whether person characteristics as trait sexual motivation, extent of SEM use in the last month, and age at onset of goal-oriented SEM use affect the neural responses to SEM and SEM cues. The trials of the fMRI experiment consisted of an expectation phase with SEM or neutral cues and a presentation phase with SEM or neutral stimuli, respectively. Analyses showed that the reward circuitry was activated by SEM, but also by SEM cues. There were some sex differences in hemodynamic responses to SEM during the presentation phase, but not during the expectation phase to SEM cues in any of the regions of interest. The influence of the investigated person characteristics was only small if existent. The results suggest that sex specific cue processing cannot explain sex differences in the use of SEM.

Neural mechanisms of perceptual decision-making and their link to neuropsychiatric symptoms in multiple sclerosis.

BACKGROUND: Decision-making (DM) capabilities are impaired in multiple sclerosis (MS). A variety of researchers hypothesized that this impairment is associated with reduced quality of life (QoL) and neuropsychiatric symptoms. Studies explicitly testing this hypothesis, however, are rare, provided inconclusive results, or evaluated only a limited selection of DM domains. Consequently, we conducted the first MS study on perceptual DM (e.g. deciding whether a car will fit into a parking lot based on a visual percept) to test this assumption. METHODS: Specifically, we used an fMRI task that measured brain activity in 30 MS patients and 19 healthy controls (HCs) while the participants repeatedly decided whether objects referenced indirectly via their written object names would fit into a shoebox to investigate neural mechanisms of perceptual DM. The objects varied in size and thus decision difficulty. From these data, we determined voxel-wise brain activity parameters reflecting (i) decision difficulty and (ii) decision speed and related them to behavioral DM performance, QoL, mild to moderate depressive symptoms, and fatigue. RESULTS: Patients showed reduced DM performance. Activity reflecting decision difficulty in the middle temporal gyrus was negatively related to DM performance across MS patients and HCs; activity reflecting decision speed in MS patients was associated with depressive symptoms and fatigue in areas of the dorsal visual stream. CONCLUSION: The study shows that the perceptual DM capacity is reduced in MS. Moreover, the link between neural mechanisms of perceptual DM and neuropsychiatric symptoms suggests that an impairment in this domain is clinically relevant.

Interactions between neural decision-making circuits predict long-term dietary treatment success in obesity.

Although dietary decision-making is regulated by multiple interacting neural controllers, their impact on dietary treatment success in obesity has only been investigated individually. Here, we used fMRI to test how well interactions between the Pavlovian system (automatically triggering urges of consumption after food cue exposure) and the goal-directed system (considering long-term consequences of food decisions) predict future dietary success achieved in 39 months. Activity of the Pavlovian system was measured with a cue-reactivity task by comparing perception of food versus control pictures, activity of the goal-directed system with a food-specific delay discounting paradigm. Both tasks were applied in 30 individuals with obesity up to five times: Before a 12-week diet, immediately thereafter, and at three annual follow-up visits. Brain activity was analyzed in two steps. In the first, we searched for areas involved in Pavlovian processes and goal-directed control across the 39-month study period with voxel-wise linear mixed-effects (LME) analyses. In the second, we computed network parameters reflecting the covariation of longitudinal voxel activity (i.e. principal components) in the regions identified in the first step and used them to predict body mass changes across the 39 months with LME models. Network analyses testing the link of dietary success with activity of the individual systems as reference found a moderate negative link to Pavlovian activity primarily in left hippocampus and a moderate positive association to goal-directed activity primarily in right inferior parietal gyrus. A cross-paradigm network analysis that integrated activity measured in both tasks revealed a strong positive link for interactions between visual Pavlovian areas and goal-directed decision-making regions mainly located in right insular cortex. We conclude that adaptation of food cue processing resources to goal-directed control activity is an important prerequisite of sustained dietary weight loss, presumably since the latter activity can modulate Pavlovian urges triggered by frequent cue exposure in everyday life.