Det biologiske grunnlaget for kognitiv kontroll : en studie med hjernemorfometri, diffusion tensor imaging og error-related negativity

Bakgrunn: Kognitive kontrollfunksjoner involverer vidt distribuerte nevrale nettverk i hjernen. Disse består av differensierte kortikale områder samt fiberbanene som forbinder dem. Det har ikke vært studert hvordan variasjon i kortikal tykkelse og integriteten til underliggende hvitsubstans kan predikere kognitiv funksjon. I denne studien ble hjernemorfometri og hvitsubstansintegritet brukt for å predikere kognitive kontrollfunksjoner, operasjonalisert både ved hjelp av hendelsesrelaterte potensialer (ERP) og nevropsykologiske atferdstester. Metode: En gruppe middelaldrende friske deltakere (n = 105, alder = 40-60 år) gjennomgikk strukturell magnetisk resonanstomografi (MRI), diffusion tensor imaging (DTI), en ERP-oppgave som utløste event-related negativity (ERN) ved gale responser og sju nevropsykologiske tester relatert til tre ulike kontrollfunksjoner: inhibisjon, oppdatering og skifting. ERN-amplityde og skårer på de tre kontrolldimensjonene ble korrelert med kortikal tykkelse og fraksjonell anisotropi (FA) kalkulert fra DTI-opptakene. Resultater: De tre kognitive kontrolldimensjonene viste lave interkorrelasjoner, og ingen korrelasjon med ERN, noe som indikerer at de reflekterer ulike funksjoner. Analyser av sammenhengen mellom mål på kognitiv kontroll og kortikal tykkelse avdekket effekter i spesifikke områder av hjernebarken. ERN korrelerte negativt med tykkelse bilateralt i anterior cingulate, og viste en opphopning av voksler som korrelerte positivt med konnektivitet i den underliggende hvitsubstansen. Inhibisjon korrelerte positivt med tykkelse medialt frontalt i venstre hemisfære, og viste opphopning av voksler med negative korrelasjoner med konnektivitet i cingulum. Skifting korrelerte negativt med tykkelse temporalt og frontalt, og viste opphopning av voksler med positive korrelasjoner med konnektivitet i forceps frontalis og occipitalis. Oppdatering korrelerte negativt med tykkelse dorsolateralt frontalt og positivt med global konnektivitet. Konklusjon: Konvergerende evidens fra ulike metoder indikerer at kognitive kontrollfunksjoner i ulik grad er sensitive for makro- og mikroanatomisk variasjon i hjernestruktur hos friske personer. Effektene var imidlertid ikke sterke, og retningen på flere av sammenhengene var overraskende. Det diskuteres generelt i hvilken grad strukturelle variabler kan forventes å predikere variasjon i kognitive kontrollfunksjoner hos friske deltakere, og det mulige nevrobiologiske grunnlaget for slike sammenhenger

Exposure Quality in Cognitive Behavioral Treatment for Youth Anxiety Disorders—Predictors and Associations with Outcomes

To optimize cognitive behavioral therapy (CBT) outcomes for anxiety disorders in youth, more knowledge is needed about how specific CBT components work. Exposure to feared situations is an effective CBT component. However, there is little observation-based empirical research on how exposure relates to outcomes and other clinical variables. In a randomized controlled community clinic trial for youth with anxiety disorders, observers reliably rated exposure quality for 68 youths aged 8 to 15 years based on 118 videotaped sessions. The treatment program was the manual-based FRIENDS program. Three exposure quality elements (preparation, post-processing, and parent contribution to exposure) were examined in relation to pre-treatment demographic and clinical variables, outcomes, and youth- and therapist-rated alliance using multilevel hierarchical regression models. The outcomes were diagnostic recovery, clinical severity and anxiety symptoms change from pre- to post-treatment and one-year follow-up, and treatment dropout. The results showed that parent contribution to exposure was higher for boys and younger children. Parent contribution to exposure, but no other exposure element, was associated with a larger likelihood of diagnostic recovery and larger clinical severity reduction at one-year follow-up. Exposure quality was unrelated to outcomes at post-treatment, dropout, or alliance. We conclude that enhancing parent contribution to exposure during treatment could improve long-term outcomes after CBT for youth anxiety disorders. Exposure elements should be observed in larger samples to further examine their potential role for CBT outcomes.publishedVersio

Deviations from normative brain white and gray matter structure are associated with psychopathology in youth

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Cortical abnormalities in bipolar disorder: An MRI analysis of 6,503 individuals from the ENIGMA-Bipolar Disorder Working Group

Despite decades of research, the pathophysiology of bipolar disorder (BD) is still not well understood. Structural brain differences have been associated with BD, but results from neuroimaging studies have been inconsistent. To address this, we performed the largest study to date of cortical gray matter thickness and surface area measures from brain MRI scans of 6,503 individuals including 1,837 unrelated adults with BD and 2,582 unrelated healthy controls for group differences while also examining the effects of commonly prescribed medications, age of illness onset, history of psychosis, mood state, age and sex differences on cortical regions. In BD, cortical gray matter was thinner in frontal, temporal and parietal regions of both brain hemispheres. BD had the strongest effects on left pars opercularis (Cohen’s d = -0.293; P = 1.71x10-21), left fusiform gyrus (d = -0.288; P = 8.25x10-21), and left rostral middle frontal cortex (d = -0.276; P = 2.99x10-19). Longer duration of illness (after accounting for age at time of scanning) was associated with reduced cortical thickness in frontal, medial parietal, and occipital regions. We found that several commonly prescribed medications, including lithium, antiepileptic, and antipsychotic treatment showed significant associations with cortical thickness and surface area, even after accounting for patients who received multiple medications. Further, we did not detect cortical differences associated with a history of psychosis or mood state at the time of scanning. Our analysis revealed previously undetected associations and provides an extensive analysis of potential confounding variables in neuroimaging studies of BD

Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias

Machine learning has been increasingly applied to neuroimaging data to predict age, deriving a personalized biomarker with potential clinical applications. The scientific and clinical value of these models depends on their applicability to independently acquired scans from diverse sources. Accordingly, we evaluated the generalizability of two brain age models that were trained across the lifespan by applying them to three distinct early-life samples with participants aged 8-22 years. These models were chosen based on the size and diversity of their training data, but they also differed greatly in their processing methods and predictive algorithms. Specifically, one brain age model was built by applying gradient tree boosting (GTB) to extracted features of cortical thickness, surface area, and brain volume. The other model applied a 2D convolutional neural network (DBN) to minimally preprocessed slices of T1-weighted scans. Additional model variants were created to understand how generalizability changed when each model was trained with data that became more similar to the test samples in terms of age and acquisition protocols. Our results illustrated numerous trade-offs. The GTB predictions were relatively more accurate overall and yielded more reliable predictions when applied to lower quality scans. In contrast, the DBN displayed the most utility in detecting associations between brain age gaps and cognitive functioning. Broadly speaking, the largest limitations affecting generalizability were acquisition protocol differences and biased brain age estimates. If such confounds could eventually be removed without post-hoc corrections, brain age predictions may have greater utility as personalized biomarkers of healthy aging

Considerations on brain age predictions from repeatedly sampled data across time

Introduction Brain age, the estimation of a person's age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for the same individual at various time points and validate our findings with age-matched healthy controls. Methods We used densely sampled T1-weighted MRI data from four individuals (from two densely sampled datasets) to observe how brain age corresponds to age and is influenced by acquisition and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pretrained deep learning model. Results We found small within-subject correlations between age and brain age. We also found evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data and inconclusive effects of scan quality. Conclusion The absence of maturation effects for the age range in the presented sample, brain age model bias (including training age distribution and field strength), and model error are potential reasons for small relationships between age and brain age in densely sampled longitudinal data. Clinical applications of brain age models should consider of the possibility of apparent biases caused by variation in the data acquisition process.publishedVersio

Spherical means-based free-water volume fraction from diffusion MRI increases non-linearly with age in the white matter of the healthy human brain

Producción CientíficaThe term free-water volume fraction (FWVF) refers to the signal fraction that could be found as the cerebrospinal fluid of the brain, which has been demonstrated as a sensitive measure that correlates with cognitive performance and various neuropathological processes. It can be quantified by properly fitting the isotropic component of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using healthy subjects (178F/109M) aged 25-94, this study examines in detail the evolution of the FWVF obtained with the spherical means technique from multi-shell acquisitions in the human brain white matter across the adult lifespan, which has been previously reported to exhibit a positive trend when estimated from single-shell data using the bi-tensor signal representation. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the spherical means-based multi-shell FWVF parameter with age, at the same time, a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW corrected diffusion tensor imaging (DTI) leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to the studies conducted over the standard (single-component) DTI. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the conceivable reason for the variations observed in FA and MD parameters across the lifespan with previous studies under the standard diffusion tensor imaging.Ministerio de Ciencia e Innovación (MCIN-AEI) y FEDER-UE (grant PID2021-124407NB-I00)Ministerio de Ciencia e Innovación (MCIN-AEI) - Unión Europea “NextGenerationEU/PRTR” (grant TED2021-130758B-I00)Ministry of Science and Higher Education (Poland) - Bekker programme (grant PPN/BEK/2019/1/00421)Norwegian ExtraFoundation for Health and Rehabilitation (2015/FO5146)European Union's Horizon 2020 research and Innovation program (ERC 802998

Brain‐wide associations between white matter and age highlight the role of fornix microstructure in brain ageing

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age-associations of WM features from different diffusion approaches, we analyzed UK Biobank diffusion magnetic resonance imaging (dMRI) data across midlife and older age (N = 35,749, 44.6–82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities, and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.publishedVersio

Adherence, Competence, and Alliance as Predictors of Long-term Outcomes of Cognitive Behavioral Therapy for Youth Anxiety Disorders

The present study investigated therapist adherence, therapist competence, and patient-therapist alliance as predictors of long-term outcomes of cognitive behavioral therapy (CBT) for anxiety disorders in youth. Potential differential effects for group versus individual CBT, for therapists with or without formal CBT training, and based on youth symptom severity were examined. Videotapes (n = 181) from treatment sessions in a randomized controlled effectiveness trial comprising youth (N = 170, M age = 11.6 years, SD = 2.1) with anxiety disorders were assessed for therapist adherence and competence. Alliance was rated by therapists and youth. Participants completed a diagnostic interview and an anxiety symptom measure at pre-treatment, post-treatment, one-year follow-up, and long-term follow-up (M = 3.9 years post-treatment, SD = 0.8, range = 2.2–5.9 years). The change in anxiety symptoms or diagnostic status from pre-treatment to long-term follow-up was not significantly related to any predictor variables. However, several interaction effects were found. For loss of principal diagnosis, therapist competence predicted positive outcome when therapist adherence also was high. Adherence was found to predict positive outcome if CBT was provided individually. Therapist-rated alliance was related to both loss of principal diagnosis and loss of all diagnoses when CBT was provided in groups. Interaction effects suggested that therapists displaying both high adherence and high competence produced better long-term outcomes. Further, the alliance may be particularly important for outcomes in group CBT, whereas adherence may be particularly important for outcomes in individual CBT.publishedVersio