Prenatal tobacco exposure on brain morphometry partially mediated poor cognitive performance in preadolescent children.

Objectives: To evaluate whether prenatal tobacco exposure (PTE) is related to poorer cognitive performance, abnormal brain morphometry, and whether poor cognitive performance is mediated by PTE-related structural brain differences. Methods: The Adolescent Brain Cognitive Development study dataset was used to compare structural MRI data and neurocognitive (NIH Toolbox®) scores in 9-to-10-year-old children with (n=620) and without PTE (n=10,989). We also evaluated whether PTE effects on brain morphometry mediated PTE effects on neurocognitive scores. Group effects were evaluated using Linear Mixed Models, covaried for socio-demographics and prenatal exposures to alcohol and/or marijuana, and corrected for multiple comparisons using the false-discovery rate (FDR). Results: Compared to unexposed children, those with PTE had poorer performance (all p-values <0.05) on executive function, working memory, episodic memory, reading decoding, crystallized intelligence, fluid intelligence and overall cognition. Exposed children also had thinner parahippocampal gyri, smaller surface areas in the posterior-cingulate and pericalcarine cortices; the lingual and inferior parietal gyri, and smaller thalamic volumes (all p-values <0.001). Furthermore, among children with PTE, girls had smaller surface areas in the superior-frontal (interaction-FDR-p=0.01), precuneus (interaction-FDR-p=0.03) and postcentral gyri (interaction-FDR-p=0.02), while boys had smaller putamen volumes (interaction-FDR-p=0.02). Smaller surface areas across regions of the frontal and parietal lobes, and lower thalamic volumes, partially mediated the associations between PTE and poorer neurocognitive scores (p-values <0.001). Conclusions: Our findings suggest PTE may lead to poorer cognitive performance and abnormal brain morphometry, with sex-specific effects in some brain regions, in pre-adolescent children. The poor cognition in children with PTE may result from the smaller areas and subcortical brain volumes.

Neuronal and Glial Metabolite Abnormalities in Participants With Persistent Neuropsychiatric Symptoms After COVID-19: A Brain Proton Magnetic Resonance Spectroscopy Study.

BACKGROUND: The aim of this study was to determine whether neurometabolite abnormalities indicating neuroinflammation and neuronal injury are detectable in individuals post-coronavirus disease 2019 (COVID-19) with persistent neuropsychiatric symptoms. METHODS: All participants were studied with proton magnetic resonance spectroscopy at 3 T to assess neurometabolite concentrations (point-resolved spectroscopy, relaxation time/echo time = 3000/30 ms) in frontal white matter (FWM) and anterior cingulate cortex-gray matter (ACC-GM). Participants also completed the National Institutes of Health Toolbox cognition and motor batteries and selected modules from the Patient-Reported Outcomes Measurement Information System. RESULTS: Fifty-four participants were evaluated: 29 post-COVID-19 (mean ± SD age, 42.4 ± 12.3 years; approximately 8 months from COVID-19 diagnosis; 19 women) and 25 controls (age, 44.1 ± 12.3 years; 14 women). When compared with controls, the post-COVID-19 group had lower total N-acetyl compounds (tNAA; ACC-GM: -5.0%, P = .015; FWM: -4.4%, P = .13), FWM glutamate + glutamine (-9.5%, P = .001), and ACC-GM myo-inositol (-6.2%, P = .024). Additionally, only hospitalized patients post-COVID-19 showed age-related increases in myo-inositol, choline compounds, and total creatine (interaction P = .029 to <.001). Across all participants, lower FWM tNAA and higher ACC-GM myo-inositol predicted poorer performance on several cognitive measures (P = .001-.009), while lower ACC-GM tNAA predicted lower endurance on the 2-minute walk (P = .005). CONCLUSIONS: In participants post-COVID-19 with persistent neuropsychiatric symptoms, the lower-than-normal tNAA and glutamate + glutamine indicate neuronal injury, while the lower-than-normal myo-inositol reflects glial dysfunction, possibly related to mitochondrial dysfunction and oxidative stress in Post-COVID participants with persistent neuropsychiatric symptoms.

Ongoing oxidative stress in individuals with post-acute sequelae of COVID-19.

Objectives: Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection is associated with lower plasma glutathione (GSH) levels due to oxidative stress. However, plasma levels may not reflect brain GSH levels. Individuals with post-acute sequelae of COVID-19 (PASC) have a higher prevalence of cognitive fatigue, which might be related to altered brain γ-aminobutyric-acid (GABA) levels. Hence, our study aims to measure the brain GSH and GABA levels in PASC. Methods: 29 PASC participants and 24 uninfected controls were recruited for this study. Each was evaluated with detailed neuropsychiatric assessments and an edited proton MRS (Hadamard Encoding and Reconstruction of Mega-Edited Spectroscopy, HERMES) method to measure GABA and GSH concentrations in predominantly grey matter (GM) and predominantly white matter (WM) brain frontal voxels. Results: PASC participants were 219 ± 137 days since their COVID-19 diagnosis. Nine individuals with PASC were hospitalized. Compared to controls, individuals with PASC had similar levels of GABA in both brain regions, but lower GSH and greater age-related GSH decline in the frontal GM region. Conclusions: The lower-than-normal frontal GM GSH level in participants with PASC suggest that they have ongoing oxidative stress in the brain, and that older individuals may be even more vulnerable to oxidative stress.

Quantifying the Neuropsychiatric Symptoms in Post-Acute Sequelae of COVID-19 (PASC) using the NIH Toolbox® and PROMIS.

Objective: To quantify neuropsychiatric symptoms reported by individuals with Post-Acute Sequelae of COVID-19 (PASC) using the NIH Toolbox® for Assessment of Neurological and Behavioral Function (NIHTB) and Patient-Reported Outcomes Measurement Information System (PROMIS). Methods: 30 PASC (20 women, 21-63 years) and 27 control (16 women, 25-68 years) participants completed three NIHTB batteries and selected PROMIS tests. Group differences on fully corrected T-scores were evaluated using analysis of covariance and Cohen's d effect sizes. A linear regression model predicted the effects from time since diagnosis. Results: PASC had poorer emotional health and motor function than controls, including poorer locomotion, endurance and dexterity, but normal cognitive function, ~7 months post-infection, compared to controls. PASC participants had a steeper age-related decline on 2-Minute Walk than controls. T-scores on four cognitive and three motor tests improved with longer time since diagnosis. Conclusion: NIHTB and PROMIS captured the poorer emotional health and motor function in PASC, including the novel findings of deficits locomotion and dexterity. The normal cognitive performance suggests subclinical effects that may be compensated by neural and cognitive reserves, and manifested subjectively by the negative psychological effects and fatigue. The persistent emotional and psychiatric symptoms necessitate mental health treatment be prioritized.

Changes in Brain Activation Patterns During Working Memory Tasks in People With Post-COVID Condition and Persistent Neuropsychiatric Symptoms.

BACKGROUND AND OBJECTIVES: Post-COVID condition (PCC) is common and often involves neuropsychiatric symptoms. This study aimed to use blood oxygenation level-dependent fMRI (BOLD-fMRI) to assess whether participants with PCC had abnormal brain activation during working memory (WM) and whether the abnormal brain activation could predict cognitive performance, motor function, or psychiatric symptoms. METHODS: The participants with PCC had documented coronavirus disease 2019 (COVID-19) at least 6 weeks before enrollment. Healthy control participants had no prior history of COVID-19 and negative tests for severe acute respiratory syndrome coronavirus 2. Participants were assessed using 3 NIH Toolbox (NIHTB) batteries for Cognition (NIHTB-CB), Emotion (NIHTB-EB), and Motor function (NIHTB-MB) and selected tests from the Patient-Reported Outcomes Measurement Information System (PROMIS). Each had BOLD-fMRI at 3T, during WM (N-back) tasks with increasing attentional/WM load. RESULTS: One hundred sixty-nine participants were screened; 50 fulfilled the study criteria and had complete and usable data sets for this cross-sectional cohort study. Twenty-nine participants with PCC were diagnosed with COVID-19 242 ± 156 days earlier; they had similar ages (42 ± 12 vs 41 ± 12 years), gender proportion (65% vs 57%), racial/ethnic distribution, handedness, education, and socioeconomic status, as the 21 uninfected healthy controls. Despite the high prevalence of memory (79%) and concentration (93%) complaints, the PCC group had similar performance on the NIHTB-CB as the controls. However, participants with PCC had greater brain activation than the controls across the network (false discovery rate-corrected p = 0.003, Tmax = 4.17), with greater activation in the right superior frontal gyrus (p = 0.009, Cohen d = 0.81, 95% CI 0.15-1.46) but lesser deactivation in the default mode regions (p = 0.001, d = 1.03, 95% CI 0.61-1.99). Compared with controls, participants with PCC also had poorer dexterity and endurance on the NIHTB-MB, higher T scores for negative affect and perceived stress, but lower T scores for psychological well-being on the NIHTB-EB, as well as more pain symptoms and poorer mental and physical health on measures from the PROMIS. Greater brain activation predicted poorer scores on measures that were abnormal on the NIHTB-EB. DISCUSSION: Participants with PCC and neuropsychiatric symptoms demonstrated compensatory neural processes with greater usage of alternate brain regions, and reorganized networks, to maintain normal performance during WM tasks. BOLD-fMRI was sensitive for detecting brain abnormalities that correlated with various quantitative neuropsychiatric symptoms.

Abnormal brain diffusivity in participants with persistent neuropsychiatric symptoms after COVID-19.

Objectives: We aimed to compare brain white matter integrity in participants with post-COVID-19 conditions (PCC) and healthy controls. Methods: We compared cognitive performance (NIH Toolbox®), psychiatric symptoms and diffusion tensor imaging (DTI) metrics between 23 PCC participants and 24 controls. Fractional anisotropy (FA), axial (AD), radial (RD), and mean (MD) diffusivities were measured in 9 white matter tracts and 6 subcortical regions using MRICloud. Results: Compared to controls, PCC had similar cognitive performance, but greater psychiatric symptoms and perceived stress, as well as higher FA and lower diffusivities in multiple white matter tracts (ANCOVA-p-values≤0.001-0.048). Amongst women, PCC had higher left amygdala-MD than controls (sex-by-PCC p=0.006). Regardless of COVID-19 history, higher sagittal strata-FA predicted greater fatigue (r=0.48-0.52, p<0.001) in all participants, and higher left amygdala-MD predicted greater fatigue (r=0.61, p<0.001) and anxiety (r=0.69, p<0.001) in women, and higher perceived stress (r=0.45, p=0.002) for all participants. Conclusions: Microstructural abnormalities are evident in PCC participants averaged six months after COVID-19. The restricted diffusivity (with reduced MD) and higher FA suggest enhanced myelination or increased magnetic susceptibility from iron deposition, as seen in stress conditions. The higher amygdala-MD in female PCC suggests persistent neuroinflammation, which might contribute to their fatigue, anxiety, and perceived stress.

The additive impact of cardio-metabolic disorders and psychiatric illnesses on accelerated brain aging.

Severe mental illnesses (SMI) including major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia spectrum disorder (SSD) elevate accelerated brain aging risks. Cardio-metabolic disorders (CMD) are common comorbidities in SMI and negatively impact brain health. We validated a linear quantile regression index (QRI) approach against the machine learning "BrainAge" index in an independent SSD cohort (N = 206). We tested the direct and additive effects of SMI and CMD effects on accelerated brain aging in the N = 1,618 (604 M/1,014 F, average age = 63.53 ± 7.38) subjects with SMI and N = 11,849 (5,719 M/6,130 F; 64.42 ± 7.38) controls from the UK Biobank. Subjects were subdivided based on diagnostic status: SMI+/CMD+ (N = 665), SMI+/CMD- (N = 964), SMI-/CMD+ (N = 3,765), SMI-/CMD- (N = 8,083). SMI (F = 40.47, p = 2.06 × 10-10 ) and CMD (F = 24.69, p = 6.82 × 10-7 ) significantly, independently impacted whole-brain QRI in SMI+. SSD had the largest effect (Cohen's d = 1.42) then BD (d = 0.55), and MDD (d = 0.15). Hypertension had a significant effect on SMI+ (d = 0.19) and SMI- (d = 0.14). SMI effects were direct, independent of MD, and remained significant after correcting for effects of antipsychotic medications. Whole-brain QRI was significantly (p < 10-16 ) associated with the volume of white matter hyperintensities (WMH). However, WMH did not show significant association with SMI and was driven by CMD, chiefly hypertension (p < 10-16 ). We used a simple and robust index, QRI, the demonstrate additive effect of SMI and CMD on accelerated brain aging. We showed a greater effect of psychiatric illnesses on QRI compared to cardio-metabolic illness. Our findings suggest that subjects with SMI should be among the targets for interventions to protect against age-related cognitive decline.

ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research.

The ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individual's brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features.

Translating ENIGMA schizophrenia findings using the regional vulnerability index: Association with cognition, symptoms, and disease trajectory.

Patients with schizophrenia have patterns of brain deficits including reduced cortical thickness, subcortical gray matter volumes, and cerebral white matter integrity. We proposed the regional vulnerability index (RVI) to translate the results of Enhancing Neuro Imaging Genetics Meta-Analysis studies to the individual level. We calculated RVIs for cortical, subcortical, and white matter measurements and a multimodality RVI. We evaluated RVI as a measure sensitive to schizophrenia-specific neuroanatomical deficits and symptoms and studied the timeline of deficit formations in: early (≤5 years since diagnosis, N = 45, age = 28.8 ± 8.5); intermediate (6-20 years, N = 30, age 43.3 ± 8.6); and chronic (21+ years, N = 44, age = 52.5 ± 5.2) patients and healthy controls (N = 76, age = 38.6 ± 12.4). All RVIs were significantly elevated in patients compared to controls, with the multimodal RVI showing the largest effect size, followed by cortical, white matter and subcortical RVIs (d = 1.57, 1.23, 1.09, and 0.61, all p < 10-6 ). Multimodal RVI was significantly correlated with multiple cognitive variables including measures of visual learning, working memory and the total score of the MATRICS consensus cognitive battery, and with negative symptoms. The multimodality and white matter RVIs were significantly elevated in the intermediate and chronic versus early diagnosis group, consistent with ongoing progression. Cortical RVI was stable in the three disease-duration groups, suggesting neurodevelopmental origins of cortical deficits. In summary, neuroanatomical deficits in schizophrenia affect the entire brain; the heterochronicity of their appearance indicates both the neurodevelopmental and progressive nature of this illness. These deficit patterns may be useful for early diagnosis and as quantitative targets for more effective treatment strategies aiming to alter these neuroanatomical deficit patterns.

White matter brain aging in relationship to schizophrenia and its cognitive deficit.

We hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used a machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age (Δage) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. A feature selection procedure was first employed to choose fractional anisotropy values in 34 of 43 white fiber tracts. Using these features, a machine learning model was trained based on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on Δage and whether this effect was moderated by chronological age using the regression spline model. The results showed that Δage was significantly elevated in the age > 30 group in patients (p < 0.001) but not in age ≤ 30 group (p = 0.364). Δage in patients was significantly and negatively associated with both working memory (ß = -0.176, p = 0.007) and processing speed (ß = -0.519, p = 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the Δage is elevated in SZs and become significantly from the third decade of life; the increase of Δage in SZs is associated with the declined neurocognitive performance.

Comparison of regional brain deficit patterns in common psychiatric and neurological disorders as revealed by big data.

Neurological and psychiatric illnesses are associated with regional brain deficit patterns that bear unique signatures and capture illness-specific characteristics. The Regional Vulnerability Index (RVI) was developed toquantify brain similarity by comparing individual white matter microstructure, cortical gray matter thickness and subcortical gray matter structural volume measures with neuroanatomical deficit patterns derived from large-scale meta-analytic studies. We tested the specificity of the RVI approach for major depressive disorder (MDD) and Alzheimer's disease (AD) in a large epidemiological sample of UK Biobank (UKBB) participants (N = 19,393; 9138 M/10,255F; age = 64.8 ± 7.4 years). Compared to controls free of neuropsychiatric disorders, participants with MDD (N = 2,248; 805 M/1443F; age = 63.4 ± 7.4) had significantly higher RVI-MDD values (t = 5.6, p = 1·10-8), but showed no detectable difference in RVI-AD (t = 2.0, p = 0.10). Subjects with dementia (N = 7; 4 M/3F; age = 68.6 ± 8.6 years) showed significant elevation in RVI-AD (t = 4.2, p = 3·10-5) but not RVI-MDD (t = 2.1, p = 0.10) compared to controls. Even within affective illnesses, participants with bipolar disorder (N = 54) and anxiety disorder (N = 773) showed no significant elevation in whole-brain RVI-MDD. Participants with Parkinson's disease (N = 37) showed elevation in RVI-AD (t = 2.4, p = 0.01) while subjects with stroke (N = 247) showed no such elevation (t = 1.1, p = 0.3). In summary, we demonstrated elevation in RVI-MDD and RVI-AD measures in the respective illnesses with strong replicability that is relatively specific to the respective diagnoses. These neuroanatomic deviation patterns offer a useful biomarker for population-wide assessments of similarity to neuropsychiatric illnesses.

Allostatic Load Effects on Cortical and Cognitive Deficits in Essentially Normotensive, Normoweight Patients with Schizophrenia.

Reduced cortical gray matter integrity and cognitive abilities are among core deficits in schizophrenia. We hypothesized that higher allostatic load (AL) that accounts for exposure to chronic stress is a contributor to structural and cognitive deficits in schizophrenia. One hundred and sixty-seven schizophrenia patients who were on average with normal weight, normal systolic, and diastolic blood pressure and 72 healthy controls were enrolled in the study. Group differences in subclinical cardiovascular, metabolic, immune, and neuroendocrine biological markers as indexed by AL and contribution of AL components to the structural and cognitive deficits in schizophrenia were explored. Compared with controls, schizophrenia patients who were normotensive, normoweight, and had low total cholesterol levels still had significantly higher AL mainly due to lower high-density lipoprotein cholesterol and higher heart rate, waist-hip ratio, hemoglobinA1c, hypersensitive C-reactive protein, and overnight-urine cortisol levels. Patients also had decreased whole-brain mean cortical thickness, and lower cognition assessed by the MATRICS consensus cognitive battery. AL was inversely correlated with mean cortical thickness and cognition in schizophrenia, while none of these relationships existed in controls. Mediation analyses showed the effect of AL on cognitive deficits in schizophrenia was significantly mediated by cortical thinning, and the most significant mediating cortical area was the left superior frontal gyrus. Cortical thickness may act as a mediator between AL and cognitive deficits in schizophrenia. Early intervention strategies to reduce cortical thinning and cognitive dysfunction in schizophrenia should target specific aspects of their high AL in addition to weight gain, hypertension and high cholesterol levels.

A White Matter Connection of Schizophrenia and Alzheimer's Disease.

Schizophrenia (SZ) is a severe psychiatric illness associated with an elevated risk for developing Alzheimer's disease (AD). Both SZ and AD have white matter abnormalities and cognitive deficits as core disease features. We hypothesized that aging in SZ patients may be associated with the development of cerebral white matter deficit patterns similar to those observed in AD. We identified and replicated aging-related increases in the similarity between white matter deficit patterns in patients with SZ and AD. The white matter "regional vulnerability index" (RVI) for AD was significantly higher in SZ patients compared with healthy controls in both the independent discovery (Cohen's d = 0.44, P = 1·10-5, N = 173 patients/230 control) and replication (Cohen's d = 0.78, P = 9·10-7, N = 122 patients/64 controls) samples. The degree of overlap with the AD deficit pattern was significantly correlated with age in patients (r = .21 and .29, P < .01 in discovery and replication cohorts, respectively) but not in controls. Elevated RVI-AD was significantly associated with cognitive measures in both SZ and AD. Disease and cognitive specificities were also tested in patients with mild cognitive impairment and showed intermediate overlap. SZ and AD have diverse etiologies and clinical courses; our findings suggest that white matter deficits may represent a key intersecting point for these 2 otherwise distinct diseases. Identifying mechanisms underlying this white matter deficit pattern may yield preventative and treatment targets for cognitive deficits in both SZ and AD patients.

Effects of ketamine and midazolam on resting state connectivity and comparison with ENIGMA connectivity deficit patterns in schizophrenia.

Subanesthetic administration of ketamine is a pharmacological model to elicit positive and negative symptoms of psychosis in healthy volunteers. We used resting-state pharmacological functional MRI (rsPhfMRI) to identify cerebral networks affected by ketamine and compared them to the functional connectivity (FC) in schizophrenia. Ketamine can produce sedation and we contrasted its effects with the effects of the anxiolytic drug midazolam. Thirty healthy male volunteers (age = 19-37 years) underwent a randomized, three-way, cross-over study consisting of three imaging sessions, with 48 hr between sessions. A session consisted of a control period followed by infusion of placebo or ketamine or midazolam. The ENIGMA rsfMRI pipeline was used to derive two long-distance (seed-based and dual-regression) and one local (regional homogeneity, ReHo) FC measures. Ketamine induced significant reductions in the connectivity of the salience network (Cohen's d: 1.13 ± 0.28, p = 4.0 × 10-3 ), auditory network (d: 0.67 ± 0.26, p = .04) and default mode network (DMN, d: 0.63 ± 0.26, p = .05). Midazolam significantly reduced connectivity in the DMN (d: 0.77 ± 0.27, p = .03). The effect sizes for ketamine for resting networks showed a positive correlation (r = .59, p = .07) with the effect sizes for schizophrenia-related deficits derived from ENIGMA's study of 261 patients and 327 controls. Effect sizes for midazolam were not correlated with the schizophrenia pattern (r = -.17, p = .65). The subtraction of ketamine and midazolam patterns showed a significant positive correlation with the pattern of schizophrenia deficits (r = .68, p = .03). RsPhfMRI reliably detected the shared and divergent pharmacological actions of ketamine and midazolam on cerebral networks. The pattern of disconnectivity produced by ketamine was positively correlated with the pattern of connectivity deficits observed in schizophrenia, suggesting a brain functional basis for previously poorly understood effects of the drug.

Cardiovascular risks impact human brain N-acetylaspartate in regionally specific patterns.

Cardiovascular risk factors such as dyslipidemia and hypertension increase the risk for white matter pathology and cognitive decline. We hypothesize that white matter levels of N-acetylaspartate (NAA), a chemical involved in the metabolic pathway for myelin lipid synthesis, could serve as a biomarker that tracks the influence of cardiovascular risk factors on white matter prior to emergence of clinical changes. To test this, we measured levels of NAA across white matter and gray matter in the brain using echo planar spectroscopic imaging (EPSI) in 163 individuals and examined the relationship of regional NAA levels and cardiovascular risk factors as indexed by the Framingham Cardiovascular Risk Score (FCVRS). NAA was strongly and negatively correlated with FCVRS across the brain, but, after accounting for age and sex, the association was found primarily in white matter regions, with additional effects found in the thalamus, hippocampus, and cingulate gyrus. FCVRS was also negatively correlated with creatine levels, again primarily in white matter. The results suggest that cardiovascular risks are related to neurochemistry with a predominantly white matter pattern and some subcortical and cortical gray matter involvement. NAA mapping of the brain may provide early surveillance for the potential subclinical impact of cardiovascular and metabolic risk factors on the brain.

White Matter in Schizophrenia Treatment Resistance.

OBJECTIVE: Failure of antipsychotic medications to resolve symptoms in patients with schizophrenia creates a clinical challenge that is known as treatment resistance. The causes of treatment resistance are unknown, but it is associated with earlier age at onset and more severe cognitive deficits. The authors tested the hypothesis that white matter deficits that are involved in both neurodevelopment and severity of cognitive deficits in schizophrenia are associated with a higher risk of treatment resistance. METHODS: The study sample (N=122; mean age, 38.2 years) included schizophrenia patients at treatment initiation (N=45), patients whose symptoms were treatment responsive (N=40), and patients whose symptoms were treatment resistant (N=37), as well as healthy control subjects (N=78; mean age, 39.2 years). White matter regional vulnerability index (RVI) was tested as a predictor of treatment resistance and cognitive deficits. Higher RVI is indicative of better agreement between diffusion tensor imaging fractional anisotropy across the brain in an individual and the pattern identified by the largest-to-date meta-analysis of white matter deficits in schizophrenia. RESULTS: Patients with treatment-resistant symptoms showed the highest white matter RVI (mean=0.38 [SD=0.2]), which was significantly higher than the RVI among patients with treatment-responsive symptoms (mean=0.30 [SD=0.02]). At the onset of treatment, schizophrenia patients showed significantly higher RVI than healthy control subjects (mean=0.18 [SD=0.03] and mean=0.13 [SD=0.02], respectively). RVIs were significantly correlated with performance on processing speed and negative symptoms. CONCLUSIONS: Schizophrenia affects white matter microstructure in specific regional patterns. Susceptibility to white matter regional deficits is associated with an increased likelihood of treatment resistance. Developments to overcome schizophrenia treatment resistance should consider white matter as an important target.

White matter and hypoxic hypobaria in humans.

Occupational exposure to hypobaria (low atmospheric pressure) is a risk factor for reduced white matter integrity, increased white matter hyperintensive burden, and decline in cognitive function. We tested the hypothesis that a discrete hypobaric exposure will have a transient impact on cerebral physiology. Cerebral blood flow, fractional anisotropy of water diffusion in cerebral white matter, white matter hyperintensity volume, and concentrations of neurochemicals were measured at baseline and 24 hr and 72 hr postexposure in N = 64 healthy aircrew undergoing standard US Air Force altitude chamber training and compared to N = 60 controls not exposed to hypobaria. We observed that hypobaric exposure led to a significant rise in white matter cerebral blood flow (CBF) 24 hr postexposure that remained elevated, albeit not significantly, at 72 hr. No significant changes were observed in structural measurements or gray matter CBF. Subjects with higher baseline concentrations of neurochemicals associated with neuroprotection and maintenance of normal white matter physiology (glutathione, N-acetylaspartate, glutamate/glutamine) showed proportionally less white matter CBF changes. Our findings suggest that discrete hypobaric exposure may provide a model to study white matter injury associated with occupational hypobaric exposure.

Miniature pig magnetic resonance spectroscopy model of normal adolescent brain development.

BACKGROUND: We are developing the miniature pig (Sus scrofa domestica), an in-vivo translational, gyrencephalic model for brain development, as an alternative to laboratory rodents/non-human primates. We analyzed longitudinal changes in adolescent pigs using proton magnetic resonance spectroscopy (1H-MRS) and examined the relationship with white matter (WM) integrity derived from diffusion weighted imaging (DWI). NEW METHOD: Twelve female Sinclair™ pigs underwent three imaging/spectroscopy sessions every 23.95 ± 3.73 days beginning at three months of age using a clinical 3 T scanner. 1H-MRS data were collected using 1.2 × 1.0 × 3.0 cm voxels placed in left and right hemisphere WM using a Point Resolved Spectroscopy sequence (TR = 2000 ms, TE = 30 ms). Concentrations of N-acetylaspartate, myo-inositol (MI), glutamate + glutamine, choline, creatine, and macromolecules (MM) 09 and 14 were averaged from both hemispheres. DWI data were collected using 15 shells of b-values (b = 0-3500 s/mm2) with 32 directions/shell and fit using the WM Tract Integrity model to calculate fractional anisotropy (FA), kurtosis anisotropy (KA) and permeability-diffusivity index. RESULTS: MI and MM09 significantly declined with age. Increased FA and KA significantly correlated with decline in MI and MM09. Correlations lost significance once corrected for age. COMPARISON WITH EXISTING METHODS: MRI scanners/protocols can be used to collect 1H-MRS and DWI data in pigs. Pigs have a larger, more complex, gyrencephalic brain than laboratory rodents but are less complex than non-human primates, thus satisfying the "replacement" principle of animal research. CONCLUSIONS: Longitudinal effects in MRS measurements were similar to those reported in adolescent humans. MRS changes correlated with diffusion measurements indicating ongoing WM myelination/maturation.

Integration of routine QA data into mega-analysis may improve quality and sensitivity of multisite diffusion tensor imaging studies.

A novel mega-analytical approach that reduced methodological variance was evaluated using a multisite diffusion tensor imaging (DTI) fractional anisotropy (FA) data by comparing white matter integrity in people with schizophrenia to controls. Methodological variance was reduced through regression of variance captured from quality assurance (QA) and by using Marchenko-Pastur Principal Component Analysis (MP-PCA) denoising. N = 192 (119 patients/73 controls) data sets were collected at three sites equipped with 3T MRI systems: GE MR750, GE HDx, and Siemens Trio. DTI protocol included five b = 0 and 60 diffusion-sensitized gradient directions (b = 1,000 s/mm2 ). In-house DTI QA protocol data was acquired weekly using a uniform phantom; factor analysis was used to distil into two orthogonal QA factors related to: SNR and FA. They were used as site-specific covariates to perform mega-analytic data aggregation. The effect size of patient-control differences was compared to these reported by the enhancing neuro imaging genetics meta-analysis (ENIGMA) consortium before and after regressing QA variance. Impact of MP-PCA filtering was evaluated likewise. QA-factors explained ∼3-4% variance in the whole-brain average FA values per site. Regression of QA factors improved the effect size of schizophrenia on whole brain average FA values-from Cohen's d = .53 to .57-and improved the agreement between the regional pattern of FA differences observed in this study versus ENIGMA from r = .54 to .70. Application of MP-PCA-denoising further improved the agreement to r = .81. Regression of methodological variances captured by routine QA and advanced denoising that led to a better agreement with a large mega-analytic study.

Miniature pig model of human adolescent brain white matter development.

BACKGROUND: Neuroscience research in brain development and disorders can benefit from an in vivo animal model that portrays normal white matter (WM) development trajectories and has a sufficiently large cerebrum for imaging with human MRI scanners and protocols. NEW METHOD: Twelve three-month-old Sinclair™ miniature pigs (Sus scrofa domestica) were longitudinally evaluated during adolescent development using advanced diffusion weighted imaging (DWI) focused on cerebral WM. Animals had three MRI scans every 23.95 ±â€¯3.73 days using a 3-T scanner. The DWI imaging protocol closely modeled advanced human structural protocols and consisted of fifteen b-shells (b = 0-3500 s/mm2) with 32-directions/shell. DWI data were analyzed using diffusion kurtosis and bi-exponential modeling that provided measurements that included fractional anisotropy (FA), radial kurtosis, kurtosis anisotropy (KA), axial kurtosis, tortuosity, and permeability-diffusivity index (PDI). RESULTS: Significant longitudinal effects of brain development were observed for whole-brain average FA, KA, and PDI (all p < 0.001). There were expected regional differences in trends, with corpus callosum fibers showing the highest rate of change. COMPARISON WITH EXISTING METHOD(S): Pigs have a large, gyrencephalic brain that can be studied using clinical MRI scanners/protocols. Pigs are less complex than non-human primates thus satisfying the "replacement" principle of animal research. CONCLUSIONS: Longitudinal effects were observed for whole-brain and regional diffusion measurements. The changes in diffusion measurements were interepreted as evidence for ongoing myelination and maturation of cerebral WM. Corpus callosum and superficial cortical WM showed the expected higher rates of change, mirroring results in humans.