White Matter in Schizophrenia Treatment Resistance

Zavaliangos‐Petropulu

Jahanshad

et al. 2020

Schizophrenia Bulletin

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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.

Section: Discussionmentioning

confidence: 99%

“…DTI data were collected using a 3T Siemens MRI scanner. The clinical and DTI protocol information have been reported 32 and are further described in SI.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A White Matter Connection of Schizophrenia and Alzheimer’s Disease

Zavaliangos‐Petropulu

Jahanshad

et al. 2020

Schizophrenia Bulletin

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“…Neuropathology of accelerated brain aging likely contributes to the severity of the cognitive deficits in patients that form the core of socioeconomic impairments in this illness (Kochunov et al, 2016b;Kelly et al, 2018;Kochunov et al, 2017). Previously, we have shown that white matter deficits are associated with cognitive deficits and treatment difficulty in SZ (Kochunov et al, 2019(Kochunov et al, , 2017(Kochunov et al, , 2016b and have hypothesized that accelerated white matter aging in SZ leads to development of cognitive deficits and disorder-specific deficit patterns .…”

Section: Introductionmentioning

confidence: 97%

White matter brain aging In Relationship to Schizophrenia and Its Cognitive Deficit

Wang

Sampath

et al. 2020

Preprint

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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 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. Followed feature selection, a machine learning model was trained with fractional anisotropy values in 34 of 43 tracts 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 (β = -0.176, p = 0.007) and processing speed (β = -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 middle life stage; the increase of Δage in SZs is associated with the decline neurocognitive performance.

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

Hong

Dennis

et al. 2020

Human Brain Mapping

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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), obsessivecompulsive 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.