Fractal Dimension Analysis in Neurological Disorders: An Overview.

Fractal analysis has emerged as a powerful tool for characterizing irregular and complex patterns found in the nervous system. This characterization is typically applied by estimating the fractal dimension (FD), a scalar index that describes the topological complexity of the irregular components of the nervous system, both at the macroscopic and microscopic levels, that may be viewed as geometric fractals. Moreover, temporal properties of neurophysiological signals can also be interpreted as dynamic fractals. Given its sensitivity for detecting changes in brain morphology, FD has been explored as a clinically relevant marker of brain damage in several neuropsychiatric conditions as well as in normal and pathological cerebral aging. In this sense, evidence is accumulating for decreases in FD in Alzheimer's disease, frontotemporal dementia, Parkinson's disease, multiple sclerosis, and many other neurological disorders. In addition, it is becoming increasingly clear that fractal analysis in the field of clinical neurology opens the possibility of detecting structural alterations in the early stages of the disease, which highlights FD as a potential diagnostic and prognostic tool in clinical practice.

The immersive virtual memory task: Assessing object-location memory in neurological patients using immersive virtual reality.

TRIAL REGISTRATION: German Clinical Trials Register identifier: DRKS00024005.

Increased flexibility of brain dynamics in patients with multiple sclerosis.

Patients with multiple sclerosis consistently show widespread changes in functional connectivity. Yet, alterations are heterogeneous across studies, underscoring the complexity of functional reorganization in multiple sclerosis. Here, we aim to provide new insights by applying a time-resolved graph-analytical framework to identify a clinically relevant pattern of dynamic functional connectivity reconfigurations in multiple sclerosis. Resting-state data from 75 patients with multiple sclerosis (N = 75, female:male ratio of 3:2, median age: 42.0 ± 11.0 years, median disease duration: 6 ± 11.4 years) and 75 age- and sex-matched controls (N = 75, female:male ratio of 3:2, median age: 40.2 ± 11.8 years) were analysed using multilayer community detection. Local, resting-state functional system and global levels of dynamic functional connectivity reconfiguration were characterized using graph-theoretical measures including flexibility, promiscuity, cohesion, disjointedness and entropy. Moreover, we quantified hypo- and hyper-flexibility of brain regions and derived the flexibility reorganization index as a summary measure of whole-brain reorganization. Lastly, we explored the relationship between clinical disability and altered functional dynamics. Significant increases in global flexibility (t = 2.38, PFDR = 0.024), promiscuity (t = 1.94, PFDR = 0.038), entropy (t = 2.17, PFDR = 0.027) and cohesion (t = 2.45, PFDR = 0.024) were observed in patients and were driven by pericentral, limbic and subcortical regions. Importantly, these graph metrics were correlated with clinical disability such that greater reconfiguration dynamics tracked greater disability. Moreover, patients demonstrate a systematic shift in flexibility from sensorimotor areas to transmodal areas, with the most pronounced increases located in regions with generally low dynamics in controls. Together, these findings reveal a hyperflexible reorganization of brain activity in multiple sclerosis that clusters in pericentral, subcortical and limbic areas. This functional reorganization was linked to clinical disability, providing new evidence that alterations of multilayer temporal dynamics play a role in the manifestation of multiple sclerosis.

A spatiotemporal complexity architecture of human brain activity.

The human brain operates in large-scale functional networks. These networks are an expression of temporally correlated activity across brain regions, but how global network properties relate to the neural dynamics of individual regions remains incompletely understood. Here, we show that the brain's network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous "complexity drops" in functional magnetic resonance imaging signals. These episodes closely explain functional connectivity strength between regions, subserve the propagation of neural activity patterns, and reflect interindividual differences in age and behavior. Furthermore, complexity drops define neural activity states that dynamically shape the connectivity strength, topological configuration, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a principled complexity architecture of neural activity-a human "complexome" that underpins the brain's functional network organization.

Reduced resilience of brain state transitions in anti-N-methyl-D-aspartate receptor encephalitis.

Patients with anti-N-methyl-aspartate receptor (NMDA) receptor encephalitis suffer from a severe neuropsychiatric syndrome, yet most patients show no abnormalities in routine magnetic resonance imaging. In contrast, advanced neuroimaging studies have consistently identified disrupted functional connectivity in these patients, with recent work suggesting increased volatility of functional state dynamics. Here, we investigate these network dynamics through the spatiotemporal trajectory of meta-state transitions, yielding a time-resolved account of brain state exploration in anti-NMDA receptor encephalitis. To this end, resting-state functional magnetic resonance imaging data were acquired in 73 patients with anti-NMDA receptor encephalitis and 73 age- and sex-matched healthy controls. Time-resolved functional connectivity was clustered into brain meta-states, giving rise to a time-resolved transition network graph with states as nodes and transitions between brain meta-states as weighted, directed edges. Network topology, robustness and transition cost of these transition networks were compared between groups. Transition networks of patients showed significantly lower local efficiency (t = -2.41, pFDR  = .029), lower robustness (t = -2.01, pFDR  = .048) and higher leap size (t = 2.18, pFDR  = .037) compared with controls. Furthermore, the ratio of within-to-between module transitions and state similarity was significantly lower in patients. Importantly, alterations of brain state transitions correlated with disease severity. Together, these findings reveal systematic alterations of transition networks in patients, suggesting that anti-NMDA receptor encephalitis is characterized by reduced stability of brain state transitions and that this reduced resilience of transition networks plays a clinically relevant role in the manifestation of the disease.

Functional connectivity dynamics reflect disability and multi-domain clinical impairment in patients with relapsing-remitting multiple sclerosis.

BACKGROUND & AIM: Multiple sclerosis (MS) is an autoimmune disease of the central nervous system associated with deficits in cognitive and motor functioning. While structural brain changes such as demyelination are an early hallmark of the disease, a characteristic profile of functional brain alterations in early MS is lacking. Functional neuroimaging studies at various disease stages have revealed complex and heterogeneous patterns of aberrant functional connectivity (FC) in MS, with previous studies largely being limited to a static account of FC. Thus, it remains unclear how time-resolved FC relates to variance in clinical disability status in early MS. We here aimed to characterize brain network organization in early MS patients with time-resolved FC analysis and to explore the relationship between disability status, multi-domain clinical outcomes and altered network dynamics. METHODS: Resting-state functional MRI (rs-fMRI) data were acquired from 101 MS patients and 101 age- and sex-matched healthy controls (HC). Based on the Expanded Disability Status Score (EDSS), patients were split into two sub-groups: patients without clinical disability (EDSS ≤ 1, n = 36) and patients with mild to moderate levels of disability (EDSS ≥ 2, n = 39). Five dynamic FC states were extracted from whole-brain rs-fMRI data. Group differences in static and dynamic FC strength, across-state overall connectivity, dwell time, transition frequency, modularity, and global connectivity were assessed. Patients' impairment was quantified as custom clinical outcome z-scores (higher: worse) for the domains depressive symptoms, fatigue, motor, vision, cognition, total brain atrophy, and lesion load. Correlation analyses between functional measures and clinical outcomes were performed with Spearman partial correlation analyses controlling for age. RESULTS: Patients with mild to moderate levels of disability exhibited a more widespread spatiotemporal pattern of altered FC and spent more time in a high-connectivity, low-occurrence state compared to patients without disability and HCs. Worse symptoms in all clinical outcome domains were positively associated with EDSS scores. Furthermore, depressive symptom severity was positively related to functional dynamics as measured by state-specific global connectivity and default mode network connectivity with attention networks, while fatigue and motor impairment were related to reduced frontoparietal network connectivity with the basal ganglia. CONCLUSIONS: Despite comparably low impairment levels in early MS, we identified distinct connectivity alterations between patients with mild to moderate disability and those without disability, and these changes were sensitive to clinical outcomes in multiple domains. Furthermore, time-resolved analysis uncovered alterations in network dynamics and clinical correlations that remained undetected with conventional static analyses, showing that accounting for temporal dynamics helps disentangle the relationship between functional alterations, disability status, and symptoms in early MS.

Brain connectivity fingerprinting and behavioural prediction rest on distinct functional systems of the human connectome.

The prediction of inter-individual behavioural differences from neuroimaging data is a rapidly evolving field of research focusing on individualised methods to describe human brain organisation on the single-subject level. One method that harnesses such individual signatures is functional connectome fingerprinting, which can reliably identify individuals from large study populations. However, the precise relationship between functional signatures underlying fingerprinting and behavioural prediction remains unclear. Expanding on previous reports, here we systematically investigate the link between discrimination and prediction on different levels of brain network organisation (individual connections, network interactions, topographical organisation, and connection variability). Our analysis revealed a substantial divergence between discriminatory and predictive connectivity signatures on all levels of network organisation. Across different brain parcellations, thresholds, and prediction algorithms, we find discriminatory connections in higher-order multimodal association cortices, while neural correlates of behaviour display more variable distributions. Furthermore, we find the standard deviation of connections between participants to be significantly higher in fingerprinting than in prediction, making inter-individual connection variability a possible separating marker. These results demonstrate that participant identification and behavioural prediction involve highly distinct functional systems of the human connectome. The present study thus calls into question the direct functional relevance of connectome fingerprints.

State-dependent signatures of anti-N-methyl-d-aspartate receptor encephalitis.

Traditional static functional connectivity analyses have shown distinct functional network alterations in patients with anti-N-methyl-d-aspartate receptor encephalitis. Here, we use a dynamic functional connectivity approach that increases the temporal resolution of connectivity analyses from minutes to seconds. We hereby explore the spatiotemporal variability of large-scale brain network activity in anti-N-methyl-d-aspartate receptor encephalitis and assess the discriminatory power of functional brain states in a supervised classification approach. We included resting-state functional magnetic resonance imaging data from 57 patients and 61 controls to extract four discrete connectivity states and assess state-wise group differences in functional connectivity, dwell time, transition frequency, fraction time and occurrence rate. Additionally, for each state, logistic regression models with embedded feature selection were trained to predict group status in a leave-one-out cross-validation scheme. Compared to controls, patients exhibited diverging dynamic functional connectivity patterns in three out of four states mainly encompassing the default-mode network and frontal areas. This was accompanied by a characteristic shift in the dwell time pattern and higher volatility of state transitions in patients. Moreover, dynamic functional connectivity measures were associated with disease severity and positive and negative schizophrenia-like symptoms. Predictive power was highest in dynamic functional connectivity models and outperformed static analyses, reaching up to 78.6% classification accuracy. By applying time-resolved analyses, we disentangle state-specific functional connectivity impairments and characteristic changes in temporal dynamics not detected in static analyses, offering new perspectives on the functional reorganization underlying anti-N-methyl-d-aspartate receptor encephalitis. Finally, the correlation of dynamic functional connectivity measures with disease symptoms and severity demonstrates a clinical relevance of spatiotemporal connectivity dynamics in anti-N-methyl-d-aspartate receptor encephalitis.

Combined physical and cognitive training for older adults with and without cognitive impairment: A systematic review and network meta-analysis of randomized controlled trials.

Combining physical exercise with cognitive training is a popular intervention in dementia prevention trials and guidelines. However, it remains unclear what combination strategies are most beneficial for cognitive and physical outcomes. We aimed to compare the efficacy of the three main types of combination strategies (simultaneous, sequential or exergaming) to either intervention alone or control in older adults. Randomized controlled trials of combined cognitive and physical training were included in multivariate and network meta-analyses. In cognitively healthy older adults and mild cognitive impairment, the effect of any combined intervention relative to control was small and statistically significant for overall cognitive (k = 41, Hedges' g = 0.22, 95 % CI 0.14 to 0.30) and physical function (k = 32, g = 0.25, 95 % CI 0.13 to 0.37). Simultaneous training was the most efficacious approach for cognition, followed by sequential combinations and cognitive training alone, and significantly better than physical exercise. For physical outcomes, simultaneous and sequential training showed comparable efficacy as exercise alone and significantly exceeded all other control conditions. Exergaming ranked low for both outcomes. Our findings suggest that simultaneously and sequentially combined interventions are efficacious for promoting cognitive alongside physical health in older adults, and therefore should be preferred over implementation of single-domain training.

Clinical and Magnetic Resonance Imaging Outcome Predictors in Pediatric Anti-N-Methyl-D-Aspartate Receptor Encephalitis.

OBJECTIVE: To evaluate disease symptoms, and clinical and magnetic resonance imaging (MRI) findings and to perform longitudinal volumetric MRI analyses in a European multicenter cohort of pediatric anti-N-methyl-D-aspartate receptor encephalitis (NMDARE) patients. METHODS: We studied 38 children with NMDARE (median age = 12.9 years, range =1-18) and a total of 82 MRI scans for volumetric MRI analyses compared to matched healthy controls. Mixed-effect models and brain volume z scores were applied to estimate longitudinal brain volume development. Ordinal logistic regression and ordinal mixed models were used to predict disease outcome and severity. RESULTS: Initial MRI scans showed abnormal findings in 15 of 38 (39.5%) patients, mostly white matter T2/fluid-attenuated inversion recovery hyperintensities. Volumetric MRI analyses revealed reductions of whole brain and gray matter as well as hippocampal and basal ganglia volumes in NMDARE children. Longitudinal mixed-effect models and z score transformation showed failure of age-expected brain growth in patients. Importantly, patients with abnormal MRI findings at onset were more likely to have poor outcome (Pediatric Cerebral Performance Category score > 1, incidence rate ratio = 3.50, 95% confidence interval [CI] = 1.31-9.31, p = 0.012) compared to patients with normal MRI. Ordinal logistic regression models corrected for time from onset confirmed abnormal MRI at onset (odds ratio [OR] = 9.90, 95% CI = 2.51-17.28, p = 0.009), a presentation with sensorimotor deficits (OR = 13.71, 95% CI = 2.68-24.73, p = 0.015), and a treatment delay > 4 weeks (OR = 5.15, 95% CI = 0.47-9.82, p = 0.031) as independent predictors of poor clinical outcome. INTERPRETATION: Children with NMDARE exhibit significant brain volume loss and failure of age-expected brain growth. Abnormal MRI findings, a clinical presentation with sensorimotor deficits, and a treatment delay > 4 weeks are associated with worse clinical outcome. These characteristics represent promising prognostic biomarkers in pediatric NMDARE. ANN NEUROL 2020 ANN NEUROL 2020;88:148-159.

Multidimensional Evaluation of Virtual Reality Paradigms in Clinical Neuropsychology: Application of the VR-Check Framework.

Virtual reality (VR) represents a key technology of the 21st century, attracting substantial interest from a wide range of scientific disciplines. With regard to clinical neuropsychology, a multitude of new VR applications are being developed to overcome the limitations of classical paradigms. Consequently, researchers increasingly face the challenge of systematically evaluating the characteristics and quality of VR applications to design the optimal paradigm for their specific research question and study population. However, the multifaceted character of contemporary VR is not adequately captured by the traditional quality criteria (ie, objectivity, reliability, validity), highlighting the need for an extended paradigm evaluation framework. To address this gap, we propose a multidimensional evaluation framework for VR applications in clinical neuropsychology, summarized as an easy-to-use checklist (VR-Check). This framework rests on 10 main evaluation dimensions encompassing cognitive domain specificity, ecological relevance, technical feasibility, user feasibility, user motivation, task adaptability, performance quantification, immersive capacities, training feasibility, and predictable pitfalls. We show how VR-Check enables systematic and comparative paradigm optimization by illustrating its application in an exemplary research project on the assessment of spatial cognition and executive functions with immersive VR. This application furthermore demonstrates how the framework allows researchers to identify across-domain trade-offs, makes deliberate design decisions explicit, and optimizes the allocation of study resources. Complementing recent approaches to standardize clinical VR studies, the VR-Check framework enables systematic and project-specific paradigm optimization for behavioral and cognitive research in neuropsychology.

Association of suicidal behavior with exposure to suicide and suicide attempt: A systematic review and multilevel meta-analysis.

BACKGROUND: Exposure to suicidal behavior may be associated with increased risk of suicide, suicide attempt, and suicidal ideation and is a significant public health problem. However, evidence to date has not reliably distinguished between exposure to suicide versus suicide attempt, nor whether the risk differs across suicide-related outcomes, which have markedly different public health implications. Our aim therefore was to quantitatively assess the independent risk associated with exposure to suicide and suicide attempt on suicide, suicide attempt, and suicidal ideation outcomes and to identify moderators of this risk using multilevel meta-analysis. METHODS AND FINDINGS: We systematically searched MEDLINE, Embase, PsycINFO, CINAHL, ASSIA, Sociological Abstracts, IBSS, and Social Services Abstracts from inception to 19 November 2019. Eligible studies included comparative data on prior exposure to suicide, suicide attempt, or suicidal behavior (composite measure-suicide or suicide attempt) and the outcomes of suicide, suicide attempt, and suicidal ideation in relatives, friends, and acquaintances. Dichotomous events or odds ratios (ORs) of suicide, suicide attempt, and suicidal ideation were analyzed using multilevel meta-analyses to accommodate the non-independence of effect sizes. We assessed study quality using the National Heart, Lung, and Blood Institute quality assessment tool for observational studies. Thirty-four independent studies that presented 71 effect sizes (exposure to suicide: k = 42, from 22 independent studies; exposure to suicide attempt: k = 19, from 13 independent studies; exposure to suicidal behavior (composite): k = 10, from 5 independent studies) encompassing 13,923,029 individuals were eligible. Exposure to suicide was associated with increased odds of suicide (11 studies, N = 13,464,582; OR = 3.23, 95% CI = 2.32 to 4.51, P < 0.001) and suicide attempt (10 studies, N = 121,836; OR = 2.91, 95% CI = 2.01 to 4.23, P < 0.001). However, no evidence of an association was observed for suicidal ideation outcomes (2 studies, N = 43,354; OR = 1.85, 95% CI = 0.97 to 3.51, P = 0.06). Exposure to suicide attempt was associated with increased odds of suicide attempt (10 studies, N = 341,793; OR = 3.53, 95% CI = 2.63 to 4.73, P < 0.001), but not suicide death (3 studies, N = 723; OR = 1.64, 95% CI = 0.90 to 2.98, P = 0.11). By contrast, exposure to suicidal behavior (composite) was associated with increased odds of suicide (4 studies, N = 1,479; OR = 3.83, 95% CI = 2.38 to 6.17, P < 0.001) but not suicide attempt (1 study, N = 666; OR = 1.10, 95% CI = 0.69 to 1.76, P = 0.90), a finding that was inconsistent with the separate analyses of exposure to suicide and suicide attempt. Key limitations of this study include fair study quality and the possibility of unmeasured confounders influencing the findings. The review has been prospectively registered with PROSPERO (CRD42018104629). CONCLUSIONS: The findings of this systematic review and meta-analysis indicate that prior exposure to suicide and prior exposure to suicide attempt in the general population are associated with increased odds of subsequent suicidal behavior, but these exposures do not incur uniform risk across the full range of suicide-related outcomes. Therefore, future studies should refrain from combining these exposures into single composite measures of exposure to suicidal behavior. Finally, future studies should consider designing interventions that target suicide-related outcomes in those exposed to suicide and that include efforts to mitigate the adverse effects of exposure to suicide attempt on subsequent suicide attempt outcomes.

Evaluation of the 3D fractal dimension as a marker of structural brain complexity in multiple-acquisition MRI.

Fractal analysis represents a promising new approach to structural neuroimaging data, yet systematic evaluation of the fractal dimension (FD) as a marker of structural brain complexity is scarce. Here we present in-depth methodological assessment of FD estimation in structural brain MRI. On the computational side, we show that spatial scale optimization can significantly improve FD estimation accuracy, as suggested by simulation studies with known FD values. For empirical evaluation, we analyzed two recent open-access neuroimaging data sets (MASSIVE and Midnight Scan Club), stratified by fundamental image characteristics including registration, sequence weighting, spatial resolution, segmentation procedures, tissue type, and image complexity. Deviation analyses showed high repeated-acquisition stability of the FD estimates across both data sets, with differential deviation susceptibility according to image characteristics. While less frequently studied in the literature, FD estimation in T2-weighted images yielded robust outcomes. Importantly, we observed a significant impact of image registration on absolute FD estimates. Applying different registration schemes, we found that unbalanced registration induced (a) repeated-measurement deviation clusters around the registration target, (b) strong bidirectional correlations among image analysis groups, and (c) spurious associations between the FD and an index of structural similarity, and these effects were strongly attenuated by reregistration in both data sets. Indeed, differences in FD between scans did not simply track differences in structure per se, suggesting that structural complexity and structural similarity represent distinct aspects of structural brain MRI. In conclusion, scale optimization can improve FD estimation accuracy, and empirical FD estimates are reliable yet sensitive to image characteristics.

Computing integrated information.

Integrated information theory (IIT) has established itself as one of the leading theories for the study of consciousness. IIT essentially proposes that quantitative consciousness is identical to maximally integrated conceptual information, quantified by a measure called Φmax, and that phenomenological experience corresponds to the associated set of maximally irreducible cause-effect repertoires of a physical system being in a certain state. With the current work, we provide a general formulation of the framework, which comprehensively and parsimoniously expresses Φmax in the language of probabilistic models. Here, the stochastic process describing a system under scrutiny corresponds to a first-order time-invariant Markov process, and all necessary mathematical operations for the definition of Φmax are fully specified by a system's joint probability distribution over two adjacent points in discrete time. We present a detailed constructive rule for the decomposition of a system into two disjoint subsystems based on flexible marginalization and factorization of this joint distribution. Furthermore, we show that for a given joint distribution, virtualization is identical to a flexible factorization enforcing independence between variable subsets. We then validate our formulation in a previously established discrete example system, in which we also illustrate the previously unexplored theoretical issue of quale underdetermination due to non-unique maximally irreducible cause-effect repertoires. Moreover, we show that the current definition of Φ entails its sensitivity to the shape of the conceptual structure in qualia space, thus tying together IIT's measures of quantitative and qualitative consciousness, which we suggest be better disentangled. We propose several modifications of the framework in order to address some of these issues.

Analysis of sex and gender-specific research reveals a common increase in publications and marked differences between disciplines.

BACKGROUND: The incorporation of sex and gender-specific analysis in medical research is increasing due to pressure from public agencies, funding bodies, and the clinical and research community. However, generations of knowledge and publication trends in this discipline are currently spread over distinct specialties and are difficult to analyze comparatively. METHODS: Using a text-mining approach, we have analysed sex and gender aspects in research within nine clinical subspecialties--Cardiology, Pulmonology, Nephrology, Endocrinology, Gastroenterology, Haematology, Oncology, Rheumatology, Neurology--using six paradigmatic diseases in each one. Articles have been classified into five pre-determined research categories--Epidemiology, Pathophysiology, Clinical research, Management and Outcomes. Additional information has been collected on the type of study (human/animal) and the number of subjects included. Of the 8,836 articles initially retrieved, 3,466 (39%) included sex and gender-specific research and have been further analysed. RESULTS: Literature incorporating sex/gender analysis increased over time and displays a stronger trend if compared to overall publication increase. All disciplines, but cardiology (22%), demonstrated an underrepresentation of research about gender differences in management, which ranges from 3 to 14%. While the use of animal models for identification of sex differences in basic research varies greatly among disciplines, studies involving human subjects are frequently conducted in large cohorts with more than 1,000 patients (24% of all human studies). CONCLUSIONS: Heterogeneity characterizes sex and gender-specific research. Although large cohorts are often analysed, sex and gender differences in clinical management are insufficiently investigated leading to potential inequalities in health provision and outcomes.