Left OFC Activation in Functional Near-Infrared Spectroscopy during an Inhibitory Control Task in an Early Years Sample: Integrating Stress Responses with Cognitive Function and Brain Activation.

INTRODUCTION: Previous functional near-infrared spectroscopy (fNIRS) studies using Go/No-Go (GNG) tasks have focused on brain activation in relation to cognitive processes, particularly inhibitory control (IC). The results of these studies commonly describe right hemispheric engagement of the dorsolateral, ventromedial, or inferior frontal regions of the prefrontal cortex. Considering that typical healthy cognitive development is negatively correlated with higher cortisol levels (which may alter brain development), the overarching aim of the current study was to investigate how elevated stress (due to unforeseeable events such as the pandemic) impacts early cognitive development. METHOD: In this study, we examined fNIRS data collected from a sample of children (aged 2-4 years) during a GNG task relative to the response to stressors measured via hair cortisol concentrations. We acquired data in an ecological setting (Early Childhood Education and Care) during the coronavirus pandemic. RESULTS: We found that children with higher stress levels and a less efficient IC recruited more neural terrain and our group-level analysis indicated activation in the left orbitofrontal area during IC performance. CONCLUSIONS: A contextual stressor may disrupt accuracy in the executive function of IC early in development. More research efforts are needed to understand better how an orbitofrontal network subserves goal-directed behavior.

Cognitive Effort during Visuospatial Problem Solving in Physical Real World, on Computer Screen, and in Virtual Reality.

Spatial cognition plays a crucial role in academic achievement, particularly in science, technology, engineering, and mathematics (STEM) domains. Immersive virtual environments (VRs) have the growing potential to reduce cognitive load and improve spatial reasoning. However, traditional methods struggle to assess the mental effort required for visuospatial processes due to the difficulty in verbalizing actions and other limitations in self-reported evaluations. In this neuroergonomics study, we aimed to capture the neural activity associated with cognitive workload during visuospatial tasks and evaluate the impact of the visualization medium on visuospatial task performance. We utilized functional near-infrared spectroscopy (fNIRS) wearable neuroimaging to assess cognitive effort during spatial-reasoning-based problem-solving and compared a VR, a computer screen, and a physical real-world task presentation. Our results reveal a higher neural efficiency in the prefrontal cortex (PFC) during 3D geometry puzzles in VR settings compared to the settings in the physical world and on the computer screen. VR appears to reduce the visuospatial task load by facilitating spatial visualization and providing visual cues. This makes it a valuable tool for spatial cognition training, especially for beginners. Additionally, our multimodal approach allows for progressively increasing task complexity, maintaining a challenge throughout training. This study underscores the potential of VR in developing spatial skills and highlights the value of comparing brain data and human interaction across different training settings.

Long-term stability of the cortical volumetric profile and the functional human connectome throughout childhood and adolescence.

There is compelling evidence showing that between-subject variability in several functional and structural brain features is sufficient for unique identification in adults. However, individuation of brain functional connectomes depends on the stabilization of neurodevelopmental processes during childhood and adolescence. Here, we aimed to (1) evaluate the intra-subject functional connectome stability over time for the whole brain and for large scale functional networks and (2) determine the long-term identification accuracy or 'fingerprinting' for the cortical volumetric profile and the functional connectome. For these purposes, we analysed a longitudinal cohort of 239 children and adolescents scanned in two sessions with an interval of approximately 3 years (age range 6-15 years at baseline and 9-18 years at follow-up). Corroborating previous results using short between-scan intervals in children and adolescents, we observed a moderate identification accuracy (38%) for the whole functional profile. In contrast, identification accuracy using cortical volumetric profile was 95%. Among the large-scale networks, the default-mode (26.8%), the frontoparietal (23.4%) and the dorsal-attention (27.6%) networks were the most discriminative. Our results provide further evidence for a protracted development of specific individual structural and functional connectivity profiles.

Brain age prediction: A comparison between machine learning models using region- and voxel-based morphometric data.

Brain morphology varies across the ageing trajectory and the prediction of a person's age using brain features can aid the detection of abnormalities in the ageing process. Existing studies on such "brain age prediction" vary widely in terms of their methods and type of data, so at present the most accurate and generalisable methodological approach is unclear. Therefore, we used the UK Biobank data set (N = 10,824, age range 47-73) to compare the performance of the machine learning models support vector regression, relevance vector regression and Gaussian process regression on whole-brain region-based or voxel-based structural magnetic resonance imaging data with or without dimensionality reduction through principal component analysis. Performance was assessed in the validation set through cross-validation as well as an independent test set. The models achieved mean absolute errors between 3.7 and 4.7 years, with those trained on voxel-level data with principal component analysis performing best. Overall, we observed little difference in performance between models trained on the same data type, indicating that the type of input data had greater impact on performance than model choice. All code is provided online in the hope that this will aid future research.

Is treatment-resistant schizophrenia associated with distinct neurobiological callosal connectivity abnormalities?

BACKGROUND: Resistance to antipsychotic treatment affects up to 30% of patients with schizophrenia. Although the time course of development of treatment-resistant schizophrenia (TRS) varies from patient to patient, the reasons for these variations remain unknown. Growing evidence suggests brain dysconnectivity as a significant feature of schizophrenia. In this study, we compared fractional anisotropy (FA) of brain white matter between TRS and non-treatment-resistant schizophrenia (non-TRS) patients. Our central hypothesis was that TRS is associated with reduced FA values. METHODS: TRS was defined as the persistence of moderate to severe symptoms after adequate treatment with at least two antipsychotics from different classes. Diffusion-tensor brain MRI obtained images from 34 TRS participants and 51 non-TRS. Whole-brain analysis of FA and axial, radial, and mean diffusivity were performed using Tract-Based Spatial Statistics (TBSS) and FMRIB's Software Library (FSL), yielding a contrast between TRS and non-TRS patients, corrected for multiple comparisons using family-wise error (FWE) < 0.05. RESULTS: We found a significant reduction in FA in the splenium of corpus callosum (CC) in TRS when compared to non-TRS. The antipsychotic dose did not relate to the splenium CC. CONCLUSION: Our results suggest that the focal abnormality of CC may be a potential biomarker of TRS.

Individual versus task differences in slow potential generators.

Average slow potentials (SPs) can be computed from any voluntary task, minimally involving attention to anticipated stimuli. Their topography when recorded by large electrode arrays even during simple tasks is complex, multifocal, and its generators appear to be equally multifocal and highly variable across subjects. Various sources of noise of course contaminate such averages and must contribute to the topographic complexity. Here, we report a study in which the non-averaged SP band (0 to 1 Hz) was analyzed by independent components (ICA), from 256 channel recordings of 18 subjects, during four task conditions (resting, visual attention, CPT, and Stroop). We intended to verify whether the replicable SP generators (between two separate day sessions) modeled as current density reconstruction on structural MRI sets were individual-specific, and if putative task-related differences were systematic across subjects. Typically, 3 ICA components (out of 10) explained SPs in each task and subject, and their combined generators were highly variable across subjects: although some occipito-temporal and medial temporal areas contained generators in most subjects; the overall patterns were obviously variable, with no single area common to all 18 subjects. Linear regression modeling to compare combined generators (from all ICA components) between tasks and sessions showed significantly higher correlations between the four tasks than between sessions for each task. Moreover, it was clear that no common task-specific areas could be seen across subjects. Those results represent one more instance in which individual case analyses favor the hypothesis of individual-specific patterns of cortical activity, regardless of task conditions. We discuss this hypothesis with respect to results from the beta band, from individual-case fMRI studies, and its corroboration by functional neurosurgery and the neuropsychology of focal lesions.

Neuroimaging Association Scores: reliability and validity of aggregate measures of brain structural features linked to mental disorders in youth.

In genetics, aggregation of many loci with small effect sizes into a single score improved prediction. Nevertheless, studies applying easily replicable weighted scores to neuroimaging data are lacking. Our aim was to assess the reliability and validity of the Neuroimaging Association Score (NAS), which combines information from structural brain features previously linked to mental disorders. Participants were 726 youth (aged 6-14) from two cities in Brazil who underwent MRI and psychopathology assessment at baseline and 387 at 3-year follow-up. Results were replicated in two samples: IMAGEN (n = 1627) and the Healthy Brain Network (n = 843). NAS were derived by summing the product of each standardized brain feature by the effect size of the association of that brain feature with seven psychiatric disorders documented by previous meta-analyses. NAS were calculated for surface area, cortical thickness and subcortical volumes using T1-weighted scans. NAS reliability, temporal stability and psychopathology and cognition prediction were analyzed. NAS for surface area showed high internal consistency and 3-year stability and predicted general psychopathology and cognition with higher replicability than specific symptomatic domains for all samples. They also predicted general psychopathology with higher replicability than single structures alone, accounting for 1-3% of the variance, but without directionality. The NAS for cortical thickness and subcortical volumes showed lower internal consistency and less replicable associations with behavioural phenotypes. These findings indicate the NAS based on surface area might be replicable markers of general psychopathology, but these links are unlikely to be causal or clinically useful yet.

Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder.

Graphs/networks have become a powerful analytical approach for data modeling. Besides, with the advances in sensor technology, dynamic time-evolving data have become more common. In this context, one point of interest is a better understanding of the information flow within and between networks. Thus, we aim to infer Granger causality (G-causality) between networks' time series. In this case, the straightforward application of the well-established vector autoregressive model is not feasible. Consequently, we require a theoretical framework for modeling time-varying graphs. One possibility would be to consider a mathematical graph model with time-varying parameters (assumed to be random variables) that generates the network. Suppose we identify G-causality between the graph models' parameters. In that case, we could use it to define a G-causality between graphs. Here, we show that even if the model is unknown, the spectral radius is a reasonable estimate of some random graph model parameters. We illustrate our proposal's application to study the relationship between brain hemispheres of controls and children diagnosed with Autism Spectrum Disorder (ASD). We show that the G-causality intensity from the brain's right to the left hemisphere is different between ASD and controls.

Transitions between human functional brain networks reveal complex, cost-efficient and behaviorally-relevant temporal paths.

Resting-state functional MRI activity is organized as a complex network. However, this coordinated brain activity changes with time, raising questions about its evolving temporal arrangement. Does the brain visit different configurations through time in a random or ordered way? Advances in this area depend on developing novel paradigms that would allow us to shed light on these issues. We here propose to study the temporal changes in the functional connectome by looking at transition graphs of network activity. Nodes of these graphs correspond to brief whole-brain connectivity patterns (or meta-states), and directed links to the temporal transition between consecutive meta-states. We applied this method to two datasets of healthy subjects (160 subjects and a replication sample of 54), and found that transition networks had several non-trivial properties, such as a heavy-tailed degree distribution, high clustering, and a modular organization. This organization was implemented at a low biological cost with a high cost-efficiency of the dynamics. Furthermore, characteristics of the subjects' transition graphs, including global efficiency, local efficiency and their transition cost, were correlated with cognition and motor functioning. All these results were replicated in both datasets. We conclude that time-varying functional connectivity patterns of the brain in health progress in time in a highly organized and complex order, which is related to behavior.

Combining the intersubject correlation analysis and the multivariate distance matrix regression to evaluate associations between fNIRS signals and behavioral data from ecological experiments.

The development of methods to analyze data acquired using functional near-infrared spectroscopy (fNIRS) in experiments similar to real-life situations is of great value in modern applied neuroscience. One of the most used methods to analyze fNIRS signals consists of the application of the general linear model on the observed hemodynamic signals. However, it implies limitations on the experimental design that must be constrained by triggers related to the stimuli protocols (such as block design or event related). In this work, a novel methodology is proposed to overcome such restrictions and allow more flexible protocols. The method combines the intersubject correlation analysis and the multivariate distance matrix regression to evaluate the brain-behavior relationship of subjects submitted to experiments with no trigger-based protocols. Its applicability is demonstrated throughout a naturalistic experiment about emotions conveyed by music. Thirty-two participants freely listened to instrumental excerpts from the operatic repertoire and reported the valences of the emotions conveyed by the musical segments. The method was able to find a statistically significant correlation between the subjects' fNIRS signals and valences of their emotional responses, for the excerpt that evoked the most negative valence. This result illustrates the potential of this approach as an alternative method to analyze fNIRS signals from experiments in which block design or task-related paradigms might not be suitable.

Are serum brain-derived neurotrophic factor concentrations related to brain structure and psychopathology in late childhood and early adolescence?

OBJECTIVE: Mental disorders can have a major impact on brain development. Peripheral blood concentrations of brain-derived neurotrophic factor (BDNF) are lower in adult psychiatric disorders. Serum BDNF concentrations and BDNF genotype have been associated with cortical maturation in children and adolescents. In 2 large independent samples, this study tests associations between serum BDNF concentrations, brain structure, and psychopathology, and the effects of BDNF genotype on BDNF serum concentrations in late childhood and early adolescence. METHODS: Children and adolescents (7-14 years old) from 2 cities (n = 267 in Porto Alegre; n = 273 in São Paulo) were evaluated as part of the Brazilian high-risk cohort (HRC) study. Serum BDNF concentrations were quantified by sandwich ELISA. Genotyping was conducted from blood or saliva samples using the SNParray Infinium HumanCore Array BeadChip. Subcortical volumes and cortical thickness were quantified using FreeSurfer. The Development and Well-Being Behavior Assessment was used to identify the presence of a psychiatric disorder. RESULTS: Serum BDNF concentrations were not associated with subcortical volumes or with cortical thickness. Serum BDNF concentration did not differ between participants with and without mental disorders, or between Val homozygotes and Met carriers. CONCLUSIONS: No evidence was found to support serum BDNF concentrations as a useful marker of developmental differences in brain and behavior in early life. Negative findings were replicated in 2 of the largest independent samples investigated to date.

Using deep autoencoders to identify abnormal brain structural patterns in neuropsychiatric disorders: A large-scale multi-sample study.

Machine learning is becoming an increasingly popular approach for investigating spatially distributed and subtle neuroanatomical alterations in brain-based disorders. However, some machine learning models have been criticized for requiring a large number of cases in each experimental group, and for resembling a "black box" that provides little or no insight into the nature of the data. In this article, we propose an alternative conceptual and practical approach for investigating brain-based disorders which aim to overcome these limitations. We used an artificial neural network known as "deep autoencoder" to create a normative model using structural magnetic resonance imaging data from 1,113 healthy people. We then used this model to estimate total and regional neuroanatomical deviation in individual patients with schizophrenia and autism spectrum disorder using two independent data sets (n = 263). We report that the model was able to generate different values of total neuroanatomical deviation for each disease under investigation relative to their control group (p < .005). Furthermore, the model revealed distinct patterns of neuroanatomical deviations for the two diseases, consistent with the existing neuroimaging literature. We conclude that the deep autoencoder provides a flexible and promising framework for assessing total and regional neuroanatomical deviations in neuropsychiatric populations.

Network supporting contextual fear learning after dorsal hippocampal damage has increased dependence on retrosplenial cortex.

Hippocampal damage results in profound retrograde, but no anterograde amnesia in contextual fear conditioning (CFC). Although the content learned in the latter have been discussed, alternative regions supporting CFC learning were seldom proposed and never empirically addressed. Here, we employed network analysis of pCREB expression quantified from brain slices of rats with dorsal hippocampal lesion (dHPC) after undergoing CFC session. Using inter-regional correlations of pCREB-positive nuclei between brain regions, we modelled functional networks using different thresholds. The dHPC network showed small-world topology, equivalent to SHAM (control) network. However, diverging hubs were identified in each network. In a direct comparison, hubs in both networks showed consistently higher centrality values compared to the other network. Further, the distribution of correlation coefficients was different between the groups, with most significantly stronger correlation coefficients belonging to the SHAM network. These results suggest that dHPC network engaged in CFC learning is partially different, and engage alternative hubs. We next tested if pre-training lesions of dHPC and one of the new dHPC network hubs (perirhinal, Per; or disgranular retrosplenial, RSC, cortices) would impair CFC. Only dHPC-RSC, but not dHPC-Per, impaired CFC. Interestingly, only RSC showed a consistently higher centrality in the dHPC network, suggesting that the increased centrality reflects an increased functional dependence on RSC. Our results provide evidence that, without hippocampus, the RSC, an anatomically central region in the medial temporal lobe memory system might support CFC learning and memory.

Caudate volume differences among treatment responders, non-responders and controls in children with obsessive-compulsive disorder.

Treatment response in obsessive-compulsive disorder (OCD) is heterogeneous and the neurobiological underpinnings of such variability are unknown. To investigate this issue, we looked for differences in brain structures possibly associated with treatment response in children with OCD. 29 children with OCD (7-17 years) and 28 age-matched controls underwent structural magnetic resonance imaging. Patients then received treatment with fluoxetine or group cognitive-behavioral therapy during 14 weeks, and were classified as treatment responders or non-responders. The caudate nucleus, thalamus and orbitofrontal cortex were selected a priori, according to previous evidence of their association with OCD and its treatment. Gray matter (GM) volume comparisons between responders, non-responders and controls were performed, controlling for total GM volume. 17 patients were classified as responders. Differences among responders, non-responders and controls were found in both caudate nuclei (both p-values = 0.041), but after Bonferroni correction for multiple comparisons, these findings were non-significant. However, after excluding the effect of an outlier, findings were significant for the right caudate (p = 0.004). Pairwise comparisons showed larger caudate GM volume in responders versus non-responders and controls, bilaterally. The right caudate accounted for 20.2% of the variance in Y-BOCS changes after treatment in a linear regression model, with a positive correlation (p = 0.016). We present a possible neural substrate for treatment response in pediatric OCD, which is in line with previous evidence regarding the caudate nucleus. Considering the limitations, further research is needed to replicate this finding and elucidate the heterogeneity of treatment response in children with OCD (National Clinical Trials Registration Number: NCT01148316).

Associations between children's family environment, spontaneous brain oscillations, and emotional and behavioral problems.

The family environment in childhood has a strong effect on mental health outcomes throughout life. This effect is thought to depend at least in part on modifications of neurodevelopment trajectories. In this exploratory study, we sought to investigate whether a feasible resting-state fMRI metric of local spontaneous oscillatory neural activity, the fractional amplitude of low-frequency fluctuations (fALFF), is associated with the levels of children's family coherence and conflict. Moreover, we sought to further explore whether spontaneous activity in the brain areas influenced by family environment would also be associated with a mental health outcome, namely the incidence of behavioral and emotional problems. Resting-state fMRI data from 655 children and adolescents (6-15 years old) were examined. The quality of the family environment was found to be positively correlated with fALFF in the left temporal pole and negatively correlated with fALFF in the right orbitofrontal cortex. Remarkably, increased fALFF in the temporal pole was associated with a lower incidence of behavioral and emotional problems, whereas increased fALFF in the orbitofrontal cortex was correlated with a higher incidence.

Beyond the target area: an integrative view of tDCS-induced motor cortex modulation in patients and athletes.

Transcranial Direct Current Stimulation (tDCS) is a non-invasive technique used to modulate neural tissue. Neuromodulation apparently improves cognitive functions in several neurologic diseases treatment and sports performance. In this study, we present a comprehensive, integrative review of tDCS for motor rehabilitation and motor learning in healthy individuals, athletes and multiple neurologic and neuropsychiatric conditions. We also report on neuromodulation mechanisms, main applications, current knowledge including areas such as language, embodied cognition, functional and social aspects, and future directions. We present the use and perspectives of new developments in tDCS technology, namely high-definition tDCS (HD-tDCS) which promises to overcome one of the main tDCS limitation (i.e., low focality) and its application for neurological disease, pain relief, and motor learning/rehabilitation. Finally, we provided information regarding the Transcutaneous Spinal Direct Current Stimulation (tsDCS) in clinical applications, Cerebellar tDCS (ctDCS) and its influence on motor learning, and TMS combined with electroencephalography (EEG) as a tool to evaluate tDCS effects on brain function.

Hand motor learning in a musical context and prefrontal cortex hemodynamic response: a functional near-infrared spectroscopy (fNIRS) study.

Due to movement automatization, the engagement of high-order cognitive processing during the motor execution of a task is expected to decrease over repetitions and practice. In this study, we assessed single session changes in the prefrontal hemodynamic signals in response to training a piano chord progression in an ecological experimental setting. We acquired functional near-infrared spectroscopy signals from 15 subjects without any previous experience on playing keyboard instruments. Our findings were that oxygenated hemoglobin changes at orbitofrontal cortex followed an inverted U-shaped curve over task execution, while the subjects' performance presented a steady slope. These results suggest an initial executive function engagement followed by facilitation of motor execution over time.

Morphometric MRI features and surgical outcome in patients with epilepsy related to hippocampal sclerosis and low intellectual quotient.

OBJECTIVE: The objectives of this study were to verify in a series of patients with mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) if those with low intellectual quotient (IQ) levels have more extended areas of atrophy compared with those with higher IQ levels and to analyze whether IQ could be a variable implicated on a surgical outcome. MATERIAL AND METHODS: Patients (n=106) with refractory MTLE-HS submitted to corticoamygdalohippocampectomy (CAH) (57 left mesial temporal lobe epilepsy (MTLE); 45 males) were enrolled. To determine if the IQ was a predictor of seizure outcome, totally seizure-free (SF) versus nonseizure-free (NSF) patients were evaluated. FreeSurfer was used for cortical thickness and volume estimation, comparing groups with lower (<80) and higher IQ (90-109) levels. RESULTS: In the whole series, 42.45% of patients were SF (Engel Class 1a; n=45), and 57.54% were NSF (n=61). Total cortical volume was significantly reduced in the group with lower IQ (p=0.01). Significant reductions in the left hemisphere included the following: rostral middle frontal (p=0.001), insula (p=0.002), superior temporal gyrus (p=0.003), thalamus (p=0.004), and precentral gyrus (p=0.02); and those in the right hemisphere included the following: rostral middle frontal (p=0.003), pars orbitalis (p=0.01), and insula (p=0.02). Cortical thickness analysis also showed reductions in the right superior parietal gyrus in patients with lower IQ. No significant relationship between IQ and seizure outcome was found. CONCLUSIONS: This is the first study of a series of patients with pure MTLE-HS, including those with low IQ and their morphometric magnetic resonance imaging (MRI) features using FreeSurfer. Although patients with lower intellectual scores presented more areas of brain atrophy, IQ was not a predictor of surgical outcome. Therefore, when evaluating seizure follow-up, low IQ in patients with MTLE-HS might not contraindicate resective surgery.

Cortical thickness in obsessive-compulsive disorder: multisite mega-analysis of 780 brain scans from six centres.

BACKGROUND: There is accumulating evidence for the role of fronto-striatal and associated circuits in obsessive-compulsive disorder (OCD) but limited and conflicting data on alterations in cortical thickness. AIMS: To investigate alterations in cortical thickness and subcortical volume in OCD. METHOD: In total, 412 patients with OCD and 368 healthy adults underwent magnetic resonance imaging scans. Between-group analysis of covariance of cortical thickness and subcortical volumes was performed and regression analyses undertaken. RESULTS: Significantly decreased cortical thickness was found in the OCD group compared with controls in the superior and inferior frontal, precentral, posterior cingulate, middle temporal, inferior parietal and precuneus gyri. There was also a group × age interaction in the parietal cortex, with increased thinning with age in the OCD group relative to controls. CONCLUSIONS: Our findings are partially consistent with earlier work, suggesting that group differences in grey matter volume and cortical thickness could relate to the same underlying pathology of OCD. They partially support a frontostriatal model of OCD, but also suggest that limbic, temporal and parietal regions play a role in the pathophysiology of the disorder. The group × age interaction effects may be the result of altered neuroplasticity.