Altered Cortical Gyrification in Adults Who Were Born Very Preterm and Its Associations With Cognition and Mental Health

Background: The last trimester of pregnancy is a critical period for the establishment of cortical gyrification, and altered folding patterns have been reported following very preterm birth (\u3c 33 weeks of gestation) in childhood and adolescence. However, research is scant on the persistence of such alterations in adulthood and their associations with cognitive and psychiatric outcomes. Methods: We studied 79 very preterm and 81 age-matched full-term control adults. T1-weighted magnetic resonance images were used to measure a local gyrification index (LGI), indicating the degree of folding across multiple vertices of the reconstructed cortical surface. Group and group-by-sex LGI differences were assessed by means of per-vertex adjustment for cortical thickness and overall intracranial volume. Within-group correlations were also computed between LGI and functional outcomes, including general intelligence (IQ) and psychopathology. Results: Very preterm adults had significantly reduced LGI in extensive cortical regions encompassing the frontal, anterior temporal, and occipitoparietal lobes. Alterations in lateral fronto-temporal-parietal and medial occipitoparietal regions were present in both men and women, although men showed more extensive alterations. In both very preterm and control adults, higher LGI was associated with higher IQ and lower psychopathology scores, with the spatial distribution of these associations substantially differing between the two groups. Conclusions: Very preterm adults’ brains are characterized by significant and widespread local hypogyria, and these alterations might be implicated in cognitive and psychiatric outcomes. Gyrification reflects an early developmental process and provides a fingerprint for very preterm birth

A multimodal imaging study of recognition memory in very preterm born adults

Very preterm (<32 weeks of gestation) birth is associated with structural brain alterationsand memory impairments throughout childhood and adolescence. Here, we used functional MRI(fMRI) to study the neuroanatomy of recognition memory in 49 very preterm-born adults and 50 con-trols (mean age: 30 years) during completion of a task involving visual encoding and recognition ofabstract pictures. T1-weighted and diffusion-weighted images were also collected. Bilateral hippocam-pal volumes were calculated and tractography of the fornix and cingulum was performed and assessedin terms of volume and hindrance modulated orientational anisotropy (HMOA). Online recognitionmemory task performance, assessed with A scores, was poorer in the very preterm compared with thecontrol group. Analysis of fMRI data focused on differences in neural activity between the recognitionand encoding trials. Very preterm born adults showed decreased activation in the right middle frontalgyrus and posterior cingulate cortex/precuneus and increased activation in the left inferior frontalgyrus and bilateral lateral occipital cortex (LOC) compared with controls. Hippocampi, fornix and cin-gulum volume was significantly smaller and fornix HMOA was lower in very preterm adults. Amongall the structural and functional brain metrics that showed statistically significant group differences,LOC activation was the best predictor of online task performance (P 5 0.020). In terms of associationbetween brain function and structure, LOC activation was predicted by fornix HMOA in the pretermgroup only (P 5 0.020). These results suggest that neuroanatomical alterations in very preterm bornindividuals may be underlying their poorer recognition memory performance

Prediction of mental effort derived from an automated vocal biomarker using machine learning in a large-scale remote sample

IntroductionBiomarkers of mental effort may help to identify subtle cognitive impairments in the absence of task performance deficits. Here, we aim to detect mental effort on a verbal task, using automated voice analysis and machine learning.MethodsAudio data from the digit span backwards task were recorded and scored with automated speech recognition using the online platform NeuroVocalixTM, yielding usable data from 2,764 healthy adults (1,022 male, 1,742 female; mean age 31.4 years). Acoustic features were aggregated across each trial and normalized within each subject. Cognitive load was dichotomized for each trial by categorizing trials at &gt;0.6 of each participants' maximum span as “high load.” Data were divided into training (60%), test (20%), and validate (20%) datasets, each containing different participants. Training and test data were used in model building and hyper-parameter tuning. Five classification models (Logistic Regression, Naive Bayes, Support Vector Machine, Random Forest, and Gradient Boosting) were trained to predict cognitive load (“high” vs. “low”) based on acoustic features. Analyses were limited to correct responses. The model was evaluated using the validation dataset, across all span lengths and within the subset of trials with a four-digit span. Classifier discriminant power was examined with Receiver Operating Curve (ROC) analysis.ResultsParticipants reached a mean span of 6.34 out of 8 items (SD = 1.38). The Gradient Boosting classifier provided the best performing model on test data (AUC = 0.98) and showed excellent discriminant power for cognitive load on the validation dataset, across all span lengths (AUC = 0.99), and for four-digit only utterances (AUC = 0.95).DiscussionA sensitive biomarker of mental effort can be derived from vocal acoustic features in remotely administered verbal cognitive tests. The use-case of this biomarker for improving sensitivity of cognitive tests to subtle pathology now needs to be examined

Correction: Different Regulation of Physiological and Tumor Angiogenesis in Zebrafish by Protein Kinase D1 (PKD1).

[This corrects the article DOI: 10.1371/journal.pone.0068033.]

Prediction of mental effort derived from an automated vocal biomarker using machine learning in a large-scale remote sample

Peer reviewed: TrueAcknowledgements: We would like thank Mustafa Somayla who contributed to software development relating to this study.IntroductionBiomarkers of mental effort may help to identify subtle cognitive impairments in the absence of task performance deficits. Here, we aim to detect mental effort on a verbal task, using automated voice analysis and machine learning.MethodsAudio data from the digit span backwards task were recorded and scored with automated speech recognition using the online platform NeuroVocalixTM, yielding usable data from 2,764 healthy adults (1,022 male, 1,742 female; mean age 31.4 years). Acoustic features were aggregated across each trial and normalized within each subject. Cognitive load was dichotomized for each trial by categorizing trials at &amp;gt;0.6 of each participants' maximum span as “high load.” Data were divided into training (60%), test (20%), and validate (20%) datasets, each containing different participants. Training and test data were used in model building and hyper-parameter tuning. Five classification models (Logistic Regression, Naive Bayes, Support Vector Machine, Random Forest, and Gradient Boosting) were trained to predict cognitive load (“high” vs. “low”) based on acoustic features. Analyses were limited to correct responses. The model was evaluated using the validation dataset, across all span lengths and within the subset of trials with a four-digit span. Classifier discriminant power was examined with Receiver Operating Curve (ROC) analysis.ResultsParticipants reached a mean span of 6.34 out of 8 items (SD = 1.38). The Gradient Boosting classifier provided the best performing model on test data (AUC = 0.98) and showed excellent discriminant power for cognitive load on the validation dataset, across all span lengths (AUC = 0.99), and for four-digit only utterances (AUC = 0.95).DiscussionA sensitive biomarker of mental effort can be derived from vocal acoustic features in remotely administered verbal cognitive tests. The use-case of this biomarker for improving sensitivity of cognitive tests to subtle pathology now needs to be examined.</jats:sec

Reinforcement of the Brain's Rich-Club Architecture Following Early Neurodevelopmental Disruption Caused by Very Preterm Birth

The second half of pregnancy is a crucial period for the development of structural brain connectivity, and an abrupt interruption of the typical processes of development during this phase caused by the very preterm birth (<33 weeks of gestation) is likely to result in long-lasting consequences. We used structural and diffusion imaging data to reconstruct the brain structural connectome in very preterm-born adults. We assessed its rich-club organization and modularity as 2 characteristics reflecting the capacity to support global and local information exchange, respectively. Our results suggest that the establishment of global connectivity patterns is prioritized over peripheral connectivity following early neurodevelopmental disruption. The very preterm brain exhibited a stronger rich-club architecture than the control brain, despite possessing a relative paucity of white matter resources. Using a simulated lesion approach, we also investigated whether putative structural reorganization takes place in the very preterm brain in order to compensate for its anatomical constraints. We found that connections between the basal ganglia and (pre-) motor regions, as well as connections between subcortical regions, assumed an altered role in the structural connectivity of the very preterm brain, and that such alterations had functional implications for information flow, rule learning, and verbal IQ

Different Regulation of Physiological and Tumor Angiogenesis in Zebrafish by Protein Kinase D1 (PKD1)

<div><p>Protein kinase D isoenzymes (PKDs, Prkds) are serine threonine kinases that belong to the CAMK superfamily. PKD1 is expressed in endothelial cells and is a major mediator of biological responses downstream of the VEGFRs that are relevant for angiogenesis such as endothelial cell migration, proliferation and tubulogenesis <i>in vitro</i>. PKDs also play a critical role in tumor development and progression, including tumor angiogenesis. However, given the plethora of signaling modules that drive angiogenesis, the precise role of PKD1 in both physiological and tumor angiogenesis <i>in vivo</i> has not been worked out so far. This study aimed at dissecting the contribution of PKD1 to physiological blood vessel formation, PKD1 was found to be widely expressed during zebrafish development. As far as physiological angiogenesis was concerned, morpholino-based silencing of PKD1 expression moderately reduced the formation of the intersomitic vessels and the dorsal longitudinal anastomotic vessel in <i>tg(fli1:EGFP)</i> zebrafish. In addition, silencing of PKD1 resulted in reduced formation of the parachordal lymphangioblasts that serves as a precursor for the developing thoracic duct. Interestingly, tumor angiogenesis was completely abolished in PKD1 morphants using the zebrafish/tumor xenograft angiogenesis assay. Our data in zebrafish demonstrate that PKD1 contributes to the regulation of physiological angiogenesis and lymphangiogenesis during zebrafish development and is essential for tumor angiogenesis.</p></div