Closing the loop between environment, brain and mental health: how far we might go in real-life assessments?

PURPOSE OF REVIEW: Environmental factors such as climate, urbanicity, and exposure to nature are becoming increasingly important influencers of mental health. Incorporating data gathered from real-life contexts holds promise to substantially enhance laboratory experiments by providing a more comprehensive understanding of everyday behaviors in natural environments. We provide an up-to-date review of current technological and methodological developments in mental health assessments, neuroimaging and environmental sensing. RECENT FINDINGS: Mental health research progressed in recent years towards integrating tools, such as smartphone based mental health assessments or mobile neuroimaging, allowing just-in-time daily assessments. Moreover, they are increasingly enriched by dynamic measurements of the environment, which are already being integrated with mental health assessments. To ensure ecological validity and accuracy it is crucial to capture environmental data with a high spatio-temporal granularity. Simultaneously, as a supplement to experimentally controlled conditions, there is a need for a better understanding of cognition in daily life, particularly regarding our brain's responses in natural settings. SUMMARY: The presented overview on the developments and feasibility of "real-life" approaches for mental health and brain research and their potential to identify relationships along the mental health-environment-brain axis informs strategies for real-life individual and dynamic assessments.

Multimodal workflows optimally predict response to repetitive transcranial magnetic stimulation in patients with schizophrenia: a multisite machine learning analysis.

The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structural MRI, clinical, sociodemographic, and genetic data using artificial intelligence. We developed and cross-validated rTMS response prediction models in patients with schizophrenia drawn from the multisite RESIS trial. The models incorporated pre-treatment sMRI, clinical, sociodemographic, and polygenic risk score (PRS) data. Patients were randomly assigned to receive active (N = 45) or sham (N = 47) rTMS treatment. The prediction target was individual response, defined as ≥20% reduction in pre-treatment negative symptom sum scores of the Positive and Negative Syndrome Scale. Our multimodal sequential prediction workflow achieved a balanced accuracy (BAC) of 94% (non-responders: 92%, responders: 95%) in the active-treated group and 50% in the sham-treated group. The clinical, clinical + PRS, and sMRI-based classifiers yielded BACs of 65%, 76%, and 80%, respectively. Apparent sadness, inability to feel, educational attainment PRS, and unemployment were most predictive of non-response in the clinical + PRS model, while grey matter density reductions in the default mode, limbic networks, and the cerebellum were most predictive in the sMRI model. Our sequential modelling approach provided superior predictive performance while minimising the diagnostic burden in the clinical setting. Predictive patterns suggest that rTMS responders may have higher levels of brain grey matter in the default mode and salience networks which increases their likelihood of profiting from plasticity-inducing brain stimulation methods, such as rTMS. The future clinical implementation of our models requires findings to be replicated at the international scale using stratified clinical trial designs.

Plasma Levels of the Cytokines B Cell-Activating Factor (BAFF) and A Proliferation-Inducing Ligand (APRIL) in Schizophrenia, Bipolar, and Major Depressive Disorder: A Cross Sectional, Multisite Study.

BACKGROUND: Immune dysfunction has been implicated in the pathogenesis of schizophrenia and other nonaffective psychosis (SCZ), bipolar spectrum disorder (BIP) and major depressive disorder (MDD). The cytokines B cell-activating factor (BAFF) and A proliferation-inducing ligand (APRIL) belong to the tumor necrosis factor (TNF) super family and are essential in orchestrating immune responses. Abnormal levels of BAFF and APRIL have been found in autoimmune diseases with CNS affection. METHODS: We investigated if plasma levels of BAFF and APRIL differed between patients with SCZ, BIP, and MDD with psychotic symptoms (n = 2009) and healthy control subjects (HC, n = 1212), and tested for associations with psychotic symptom load, controlling for sociodemographic status, antipsychotic and other psychotropic medication, smoking, body-mass-index, and high sensitivity CRP. RESULTS: Plasma APRIL level was significantly lower across all patient groups compared to HC (P < .001; Cohen's d = 0.33), and in SCZ compared to HC (P < .001; d = 0.28) and in BIP compared to HC (P < .001; d = 0.37). Lower plasma APRIL was associated with higher psychotic symptom load with nominal significance (P = .017), but not with any other clinical characteristics. Plasma BAFF was not significantly different across patient groups vs HC, but significantly higher in BIP compared to HC (P = .040; d = 0.12) and SCZ (P = .027; d = 0.10). CONCLUSIONS: These results show aberrant levels of BAFF and APRIL and association with psychotic symptoms in patients with SCZ and BIP. This suggest that dysregulation of the TNF system, mediated by BAFF and APRIL, is involved in the pathophysiology of psychotic disorders.

A Study of TNF Pathway Activation in Schizophrenia and Bipolar Disorder in Plasma and Brain Tissue.

Objective: A proinflammatory imbalance in the tumor necrosis factor (TNF) system may contribute to the pathogenesis of schizophrenia (SCZ) and bipolar disorders (BDs) and related comorbidities. We investigated the relative distribution of TNF-related molecules in blood and dorsolateral prefrontal cortex (DLPFC) in these disorders. Method: We measured plasma levels of TNF, soluble TNF receptor 1 (sTNFR1), soluble TNF receptor 2 (sTNFR2), and a disintegrin and metalloprotease-17 (ADAM17) using enzyme immunoassays and calculated the TNF/sTNFRs ratio (TNF/sTNFR1+sTNFR2) in a sample of 816 SCZ and BD spectrum patients and 624 healthy controls (HCs). TNF, TNFRSF1A (TNFR1), TNFRSF1B (TNFR2), and ADAM17 mRNA levels were determined in whole blood, and postmortem DLPFC obtained from an independent cohort (n = 80 SCZ, n = 44 BD, and n = 86 HC). Results: In peripheral blood, we show increased TNF-related measures in patients compared to HC, with an increased TNF/sTNFRs ratio (p = 6.00 × 10-5), but decreased TNF mRNA expression (p = 1 × 10-4), with no differences between SCZ and BD. Whole blood ADAM17 mRNA expression was markedly higher in BD vs SCZ patients (p = 1.40 × 10-14) and vs HC (p = 1.22 × 10-8). In postmortem DLPFC, we found no significant differences in mRNA expression of TNF pathway genes between any groups. Conclusions: SCZ and BD patients have increased plasma TNF pathway markers without corresponding increase in blood cell gene expression. ADAM17 expression in leukocytes is markedly different between the two disorders, while alterations in TNF-related gene expression in DLPFC are uncertain. Further studies are necessary to elucidate the aberrant regulation of the TNF pathway in severe mental disorders.

Lithium treatment and hippocampal subfields and amygdala volumes in bipolar disorder.

OBJECTIVES: Results from magnetic resonance imaging (MRI) studies are heterogeneous with regard to hippocampal and amygdala volume alterations in bipolar disorder (BD). Lithium treatment may influence both structures. It is unknown if lithium treatment has distinct effects on hippocampal subfield volumes and if subfield volumes change over the course of illness in BD. METHODS: MRI scans were obtained for 34 lithium-treated patients with BD (Li+), 147 patients with BD who were not treated with lithium (Non-Li), and 300 healthy controls. Hippocampal total and subfield volumes and amygdala volumes were automatically estimated using Freesurfer. General linear models were used to investigate volume differences between groups and the effects of illness course and lithium treatment. RESULTS: The Non-Li BD group displayed significantly smaller bilateral cornu ammonis (CA) 2/3 and CA4/dentate gyrus (DG) subfields, total hippocampal volumes, right CA1 and right subiculum subfields, and left amygdala volume compared to healthy controls. There were no differences between the Li+ BD and either the Non-Li BD or the healthy control groups. In patients with numerous affective episodes, Non-Li BD patients had smaller left CA1 and CA2/3 volumes compared to Li+ BD patients and healthy controls. There were positive associations between lithium treatment duration and left amygdala volume. CONCLUSIONS: Hippocampal subfield and amygdala volumes were reduced in Non-Li BD patients compared to healthy controls, whereas the Li+ BD volumes were no different from those in Non-Li BD patients or healthy controls. Over the course of BD, lithium treatment might counteract reductions specifically in the left CA1 and CA2/3 hippocampal subfields and amygdala volumes, in accordance with the suggested neuroprotective effects of lithium.

A common brain network links development, aging, and vulnerability to disease.

Several theories link processes of development and aging in humans. In neuroscience, one model posits for instance that healthy age-related brain degeneration mirrors development, with the areas of the brain thought to develop later also degenerating earlier. However, intrinsic evidence for such a link between healthy aging and development in brain structure remains elusive. Here, we show that a data-driven analysis of brain structural variation across 484 healthy participants (8-85 y) reveals a largely--but not only--transmodal network whose lifespan pattern of age-related change intrinsically supports this model of mirroring development and aging. We further demonstrate that this network of brain regions, which develops relatively late during adolescence and shows accelerated degeneration in old age compared with the rest of the brain, characterizes areas of heightened vulnerability to unhealthy developmental and aging processes, as exemplified by schizophrenia and Alzheimer's disease, respectively. Specifically, this network, while derived solely from healthy subjects, spatially recapitulates the pattern of brain abnormalities observed in both schizophrenia and Alzheimer's disease. This network is further associated in our large-scale healthy population with intellectual ability and episodic memory, whose impairment contributes to key symptoms of schizophrenia and Alzheimer's disease. Taken together, our results suggest that the common spatial pattern of abnormalities observed in these two disorders, which emerge at opposite ends of the life spectrum, might be influenced by the timing of their separate and distinct pathological processes in disrupting healthy cerebral development and aging, respectively.

Lack of association of the rs1344706 ZNF804A variant with cognitive functions and DTI indices of white matter microstructure in two independent healthy populations.

The rs1344706 single nucleotide polymorphism within intron 2 of the ZNF804A gene is strongly associated with schizophrenia and bipolar disorder. This variant has also been associated in some studies with a range of cognitive and neuroimaging phenotypes, but several studies have reported no effect on the same phenotypes in other samples. Here, we genotyped 670 healthy adult Norwegian subjects and 1753 healthy adult Swedish subjects for rs1344706, and tested for associations with cognitive phenotypes including general intellectual abilities, memory functions and cognitive inhibition. We also tested whether rs1344706 is associated with white matter microstructural properties using diffusion tensor imaging (DTI) data from 250 to 340 of the Norwegian and Swedish subjects, respectively. Whole-brain voxel-wise statistical modeling of the effect of the ZNF804A variant on two DTI indices, fractional anisotropy (FA) and radial diffusivity (RD), was performed using tract-based spatial statistics (TBSS), and commonly reported effect sizes were calculated within several large-scale white matter pathways based on neuroanatomical atlases. No significant associations were found between rs1344706 and the cognitive traits or white matter microstructure. We conclude that the rs1344706 SNP has no significant effect on these phenotypes in our two reasonably powered samples.

APOE-related biomarker profiles in non-pathological aging and early phases of Alzheimer's disease.

Individuals carrying the *E4 allele of the apolipoprotein E gene (APOE) are at increased risk of developing Alzheimer's disease (AD). However, the biological mechanisms underlying this association are still unclear because of the complexity of the pathological processes that cause AD. Furthermore, the effect of APOE genotype on development, maintenance and aging of the normal brain is poorly understood because of the strong bias toward studying disease associations. In vivo techniques such as neuroimaging and cognitive testing offer valuable insights into the effects of APOE genotype on brain structure and function in healthy and clinical populations. We review the evidence from in vivo studies that APOE *E4, in addition to increasing the chance of age-related pathological events, is associated with age-independent non-pathological changes in brain physiology, some of which make the brain less resilient to neurodegenerative processes. We argue that the interaction between the APOE-dependent non-pathological vulnerabilities and age-related pathological changes is one mechanism that can trigger neurodegeneration, resulting in AD and other complex phenotypes.

Mental time travel and default-mode network functional connectivity in the developing brain.

A core brain network is engaged in remembering the past and envisioning the future. This network overlaps with the so-called default-mode network, the activity of which increases when demands for focused attention are low. Because of their shared brain substrates, an intriguing hypothesis is that default-mode activity, measured at rest, is related to performance in separate attention-focused recall and imagination tasks. However, we do not know how functional connectivity of the default-mode network is related to individual differences in reconstruction of the past and imagination of the future. Here, we show that functional connectivity of the default-mode network in children and adolescents is related to the quality of past remembering and marginally to future imagination. These results corroborate previous findings of a common neuronal substrate for memory and imagination and provide evidence suggesting that mental time travel is modulated by the task-independent functional architecture of the default-mode network in the developing brain. A further analysis showed that local cortical arealization also contributed to explain recall of the past and imagination of the future, underscoring the benefits of studying both functional and structural properties to understand the brain basis for complex human cognition.

The effect of gradient sampling schemes on diffusion metrics derived from probabilistic analysis and tract-based spatial statistics.

PURPOSE: The purpose was to systematically evaluate the effect of diffusion gradient encoding scheme on estimated fractional anisotropy (FA), mean diffusivity (MD) and the voxel-wise probability of identifying crossing fibers in the brain. MATERIALS AND METHODS: Eight healthy volunteers (mean age 26.5±1.3 years, 5 males, 3 females) were imaged using a Spin-Echo Echo-Planar-Imaging sequence acquired with two signal averages [number of signals averaged (NSA)], 127 diffusion directions, and b-values of 750 s/mm(2) and 1500 s/mm(2). The number of diffusion gradient directions (N(d)) was reduced from the original value whilst maintaining a homogeneous gradient distribution enabling direct comparison of subsampled data sets with N(d)=15, 28, 43, 84, 112 and 127. FA and MD maps were generated and analyzed using tract-based spatial statistics. Effect of N(d) on estimated FA and MD was tested with voxel-wise statistics in 13 regions of interest. The number of voxels supporting two fiber populations (NV(2)) at different N(d) values was estimated using Bayesian estimation of diffusion parameters. RESULTS: Low FA values decreased significantly with increasing N(d) and with increasing NSA. MD was only marginally sensitive to N(d) and NSA. NV(2) increased significantly with N(d) but not with NSA. Thus, we conclude that accurate estimation of standard diffusion metrics FA and MD is mainly dependent on the signal-to-noise ratio (SNR), whereas the ability to differentiate multiple fiber populations requires a high diffusion sampling density.

Hippocampal volumes are important predictors for memory function in elderly women.

BACKGROUND: Normal aging involves a decline in cognitive function that has been shown to correlate with volumetric change in the hippocampus, and with genetic variability in the APOE-gene. In the present study we utilize 3D MR imaging, genetic analysis and assessment of verbal memory function to investigate relationships between these factors in a sample of 170 healthy volunteers (age range 46-77 years). METHODS: Brain morphometric analysis was performed with the automated segmentation work-flow implemented in FreeSurfer. Genetic analysis of the APOE genotype was determined with polymerase chain reaction (PCR) on DNA from whole-blood. All individuals were subjected to extensive neuropsychological testing, including the California Verbal Learning Test-II (CVLT). To obtain robust and easily interpretable relationships between explanatory variables and verbal memory function we applied the recent method of conditional inference trees in addition to scatterplot matrices and simple pairwise linear least-squares regression analysis. RESULTS: APOE genotype had no significant impact on the CVLT results (scores on long delay free recall, CVLT-LD) or the ICV-normalized hippocampal volumes. Hippocampal volumes were found to decrease with age and a right-larger-than-left hippocampal asymmetry was also found. These findings are in accordance with previous studies. CVLT-LD score was shown to correlate with hippocampal volume. Multivariate conditional inference analysis showed that gender and left hippocampal volume largely dominated predictive values for CVLT-LD scores in our sample. Left hippocampal volume dominated predictive values for females but not for males. APOE genotype did not alter the model significantly, and age was only partly influencing the results. CONCLUSION: Gender and left hippocampal volumes are main predictors for verbal memory function in normal aging. APOE genotype did not affect the results in any part of our analysis.