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Article ; Online: Language network lateralization is reflected throughout the macroscale functional organization of cortex.

Labache, Loïc / Ge, Tian / Yeo, B T Thomas / Holmes, Avram J

2023 Volume 14, Issue 1, Page(s) 3405

Abstract: Hemispheric specialization is a fundamental feature of human brain organization. However, it is not yet clear to what extent the lateralization of specific cognitive processes may be evident throughout the broad functional architecture of cortex. While ... ...

Abstract	Hemispheric specialization is a fundamental feature of human brain organization. However, it is not yet clear to what extent the lateralization of specific cognitive processes may be evident throughout the broad functional architecture of cortex. While the majority of people exhibit left-hemispheric language dominance, a substantial minority of the population shows reverse lateralization. Using twin and family data from the Human Connectome Project, we provide evidence that atypical language dominance is associated with global shifts in cortical organization. Individuals with atypical language organization exhibit corresponding hemispheric differences in the macroscale functional gradients that situate discrete large-scale networks along a continuous spectrum, extending from unimodal through association territories. Analyses reveal that both language lateralization and gradient asymmetries are, in part, driven by genetic factors. These findings pave the way for a deeper understanding of the origins and relationships linking population-level variability in hemispheric specialization and global properties of cortical organization.
MeSH term(s)	Humans ; Brain Mapping ; Functional Laterality ; Magnetic Resonance Imaging ; Brain ; Language
Language	English
Publishing date	2023-06-09
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-39131-y
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Article: Functional brain networks are associated with both sex and gender in children.

Dhamala, Elvisha / Bassett, Dani S / Yeo, B T Thomas / Homes, Avram J

bioRxiv : the preprint server for biology

2023

Abstract: Sex and gender are associated with human behavior throughout the lifespan and across health and disease, but whether they are associated with similar or distinct neural phenotypes is unknown. Here, we demonstrate that, in children, sex and gender are ... ...

Abstract	Sex and gender are associated with human behavior throughout the lifespan and across health and disease, but whether they are associated with similar or distinct neural phenotypes is unknown. Here, we demonstrate that, in children, sex and gender are uniquely reflected in the intrinsic functional connectivity of the brain. Unimodal networks are more strongly associated with sex while heteromodal networks are more strongly associated with gender. These results suggest sex and gender are irreducible to one another not only in society but also in biology.
Language	English
Publishing date	2023-11-15
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.11.12.566592
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Article ; Online: One Size Does Not Fit All: Methodological Considerations for Brain-Based Predictive Modeling in Psychiatry.

Dhamala, Elvisha / Yeo, B T Thomas / Holmes, Avram J

Biological psychiatry

2022 Volume 93, Issue 8, Page(s) 717–728

Abstract: Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo ... ...

Abstract	Psychiatric illnesses are heterogeneous in nature. No illness manifests in the same way across individuals, and no two patients with a shared diagnosis exhibit identical symptom profiles. Over the last several decades, group-level analyses of in vivo neuroimaging data have led to fundamental advances in our understanding of the neurobiology of psychiatric illnesses. More recently, access to computational resources and large, publicly available datasets alongside the rise of predictive modeling and precision medicine approaches have facilitated the study of psychiatric illnesses at an individual level. Data-driven machine learning analyses can be applied to identify disease-relevant biological subtypes, predict individual symptom profiles, and recommend personalized therapeutic interventions. However, when developing these predictive models, methodological choices must be carefully considered to ensure accurate, robust, and interpretable results. Choices pertaining to algorithms, neuroimaging modalities and states, data transformation, phenotypes, parcellations, sample sizes, and populations we are specifically studying can influence model performance. Here, we review applications of neuroimaging-based machine learning models to study psychiatric illnesses and discuss the effects of different methodological choices on model performance. An understanding of these effects is crucial for the proper implementation of predictive models in psychiatry and will facilitate more accurate diagnoses, prognoses, and therapeutics.
MeSH term(s)	Machine Learning ; Neuroimaging/methods ; Precision Medicine ; Psychiatry/methods ; Brain/diagnostic imaging
Language	English
Publishing date	2022-09-29
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	209434-4
ISSN	1873-2402 ; 0006-3223
ISSN (online)	1873-2402
ISSN	0006-3223
DOI	10.1016/j.biopsych.2022.09.024
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Article ; Online: Control theory illustrates the energy efficiency in the dynamic reconfiguration of functional connectivity.

Deng, Shikuang / Li, Jingwei / Thomas Yeo, B T / Gu, Shi

Communications biology

2022 Volume 5, Issue 1, Page(s) 295

Abstract: ... prediction power for the behavioral scores (Combination vs. Control: t = 9.41, p = 1.64e-13; Combination vs ... Graph: t = 4.92, p = 3.81e-6). Our approach integrates statistical inference and dynamical system ...

Abstract	The brain's functional connectivity fluctuates over time instead of remaining steady in a stationary mode even during the resting state. This fluctuation establishes the dynamical functional connectivity that transitions in a non-random order between multiple modes. Yet it remains unexplored how the transition facilitates the entire brain network as a dynamical system and what utility this mechanism for dynamic reconfiguration can bring over the widely used graph theoretical measurements. To address these questions, we propose to conduct an energetic analysis of functional brain networks using resting-state fMRI and behavioral measurements from the Human Connectome Project. Through comparing the state transition energy under distinct adjacent matrices, we justify that dynamic functional connectivity leads to 60% less energy cost to support the resting state dynamics than static connectivity when driving the transition through default mode network. Moreover, we demonstrate that combining graph theoretical measurements and our energy-based control measurements as the feature vector can provide complementary prediction power for the behavioral scores (Combination vs. Control: t = 9.41, p = 1.64e-13; Combination vs. Graph: t = 4.92, p = 3.81e-6). Our approach integrates statistical inference and dynamical system inspection towards understanding brain networks.
MeSH term(s)	Brain/diagnostic imaging ; Connectome ; Conservation of Energy Resources ; Humans ; Magnetic Resonance Imaging
Language	English
Publishing date	2022-04-01
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ISSN	2399-3642
ISSN (online)	2399-3642
DOI	10.1038/s42003-022-03196-0
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Article: Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching.

Wulan, Naren / An, Lijun / Zhang, Chen / Kong, Ru / Chen, Pansheng / Bzdok, Danilo / Eickhoff, Simon B / Holmes, Avram J / Yeo, B T Thomas

bioRxiv : the preprint server for biology

2024

Abstract: Individualized phenotypic prediction based on structural MRI is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously ... ...

Abstract	Individualized phenotypic prediction based on structural MRI is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a "meta-matching" framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants ("meta-matching finetune" and "meta-matching stacking") from our previous study to work with T1-weighted MRI data by changing the base neural network architecture to a 3D convolution neural network. We compare the two meta-matching variants with elastic net and classical transfer learning using the UK Biobank (N = 36,461), Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017) and HCP-Aging dataset (N = 656). We find that meta-matching outperforms elastic net and classical transfer learning by a large margin, both when translating models within the same dataset, as well as translating models across datasets with different MRI scanners, acquisition protocols and demographics. For example, when translating a UK Biobank model to 100 HCP-YA participants, meta-matching finetune yielded a 136% improvement in variance explained over transfer learning, with an average absolute gain of 2.6% (minimum = -0.9%, maximum = 17.6%) across 35 phenotypes. Overall, our results highlight the versatility of the meta-matching framework.
Language	English
Publishing date	2024-01-02
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.12.31.573801
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Article ; Online: A critical period plasticity framework for the sensorimotor-association axis of cortical neurodevelopment.

Larsen, Bart / Sydnor, Valerie J / Keller, Arielle S / Yeo, B T Thomas / Satterthwaite, Theodore D

Trends in neurosciences

2023 Volume 46, Issue 10, Page(s) 847–862

Abstract: To understand human brain development it is necessary to describe not only the spatiotemporal patterns of neurodevelopment but also the neurobiological mechanisms that underlie them. Human neuroimaging studies have provided evidence for a hierarchical ... ...

Abstract	To understand human brain development it is necessary to describe not only the spatiotemporal patterns of neurodevelopment but also the neurobiological mechanisms that underlie them. Human neuroimaging studies have provided evidence for a hierarchical sensorimotor-to-association (S-A) axis of cortical neurodevelopment. Understanding the biological mechanisms that underlie this program of development using traditional neuroimaging approaches has been challenging. Animal models have been used to identify periods of enhanced experience-dependent plasticity - 'critical periods' - that progress along cortical hierarchies and are governed by a conserved set of neurobiological mechanisms that promote and then restrict plasticity. In this review we hypothesize that the S-A axis of cortical development in humans is partly driven by the cascading maturation of critical period plasticity mechanisms. We then describe how recent advances in in vivo neuroimaging approaches provide a promising path toward testing this hypothesis by linking signals derived from non-invasive imaging to critical period mechanisms.
MeSH term(s)	Animals ; Humans ; Critical Period, Psychological ; Models, Animal ; Neurobiology ; Neuroimaging
Language	English
Publishing date	2023-08-28
Publishing country	England
Document type	Journal Article ; Review ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
ZDB-ID	282488-7
ISSN	1878-108X ; 0378-5912 ; 0166-2236
ISSN (online)	1878-108X
ISSN	0378-5912 ; 0166-2236
DOI	10.1016/j.tins.2023.07.007
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Article ; Online: Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research.

Chen, Ji / Patil, Kaustubh R / Yeo, B T Thomas / Eickhoff, Simon B

Biological psychiatry

2022 Volume 93, Issue 1, Page(s) 18–28

Abstract: Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental ... ...

Abstract	Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.
MeSH term(s)	Humans ; Mental Disorders ; Psychopathology ; Machine Learning ; Brain/diagnostic imaging ; Neurobiology
Language	English
Publishing date	2022-08-06
Publishing country	United States
Document type	Journal Article ; Review ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	209434-4
ISSN	1873-2402 ; 0006-3223
ISSN (online)	1873-2402
ISSN	0006-3223
DOI	10.1016/j.biopsych.2022.07.025
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Article: Sex differences in the functional network underpinnings of psychotic-like experiences in children.

Dhamala, Elvisha / Chopra, Sidhant / Ooi, Leon Qi Rong / Rubio, Jose M / Yeo, B T Thomas / Malhotra, Anil K / Holmes, Avram J

bioRxiv : the preprint server for biology

2024

Abstract: Psychotic-like experiences (PLEs) include a range of sub-threshold symptoms that resemble aspects of psychosis but do not necessarily indicate the presence of psychiatric illness. These experiences are highly prevalent in youth and are associated with ... ...

Abstract	Psychotic-like experiences (PLEs) include a range of sub-threshold symptoms that resemble aspects of psychosis but do not necessarily indicate the presence of psychiatric illness. These experiences are highly prevalent in youth and are associated with developmental disruptions across social, academic, and emotional domains. While not all youth who report PLEs develop psychosis, many develop other psychiatric illnesses during adolescence and adulthood. As such, PLEs are theorized to represent early markers of poor mental health. Here, we characterized the similarities and differences in the neurobiological underpinnings of childhood PLEs across the sexes using a large sample from the ABCD Study (n=5,260), revealing sex-specific associations between functional networks connectivity and PLEs. We find that although the networks associated with PLEs overlap to some extent across the sexes, there are also crucial differences. In females, PLEs are associated with dispersed cortical and non-cortical connections, whereas in males, they are primarily associated with functional connections within limbic, temporal parietal, somato/motor, and visual networks. These results suggest that early transdiagnostic markers of psychopathology may be distinct across the sexes, further emphasizing the need to consider sex in psychiatric research as well as clinical practice.
Language	English
Publishing date	2024-04-23
Publishing country	United States
Document type	Preprint
DOI	10.1101/2024.04.22.590660
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Article ; Online: Control theory illustrates the energy efficiency in the dynamic reconfiguration of functional connectivity

Shikuang Deng / Jingwei Li / B. T. Thomas Yeo / Shi Gu

Communications Biology, Vol 5, Iss 1, Pp 1-

2022 Volume 12

Abstract: A framework that allows for the statistical investigation of the dynamic aspect of functional connectivity derived from resting-state fMRI is developed that is shown to complementarily predict individual differences in behavioral measurements compared to ...

Abstract	A framework that allows for the statistical investigation of the dynamic aspect of functional connectivity derived from resting-state fMRI is developed that is shown to complementarily predict individual differences in behavioral measurements compared to existing approaches.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2022-04-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Nocturnal Blood Pressure Estimation from Sleep Plethysmography Using Machine Learning.

Yilmaz, Gizem / Lyu, Xingyu / Ong, Ju Lynn / Ling, Lieng Hsi / Penzel, Thomas / Yeo, B T Thomas / Chee, Michael W L

Sensors (Basel, Switzerland)

2023 Volume 23, Issue 18

Abstract: Background: Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP ... ...

Abstract	Background: Elevated nocturnal blood pressure (BP) is a risk factor for cardiovascular disease (CVD) and mortality. Cuffless BP assessment aided by machine learning could be a desirable alternative to traditional cuff-based methods for monitoring BP during sleep. We describe a machine-learning-based algorithm for predicting nocturnal BP using single-channel fingertip plethysmography (PPG) in healthy adults. Methods: Sixty-eight healthy adults with no apparent sleep or CVD (53% male), with a median (IQR) age of 29 (23-46 years), underwent overnight polysomnography (PSG) with fingertip PPG and ambulatory blood pressure monitoring (ABPM). Features based on pulse morphology were extracted from the PPG waveforms. Random forest models were used to predict night-time systolic blood pressure (SBP) and diastolic blood pressure (DBP). Results: Our model achieved the highest out-of-sample performance with a window length of 7 s across window lengths explored (60 s, 30 s, 15 s, 7 s, and 3 s). The mean absolute error (MAE ± STD) was 5.72 ± 4.51 mmHg for SBP and 4.52 ± 3.60 mmHg for DBP. Similarly, the root mean square error (RMSE ± STD) was 6.47 ± 1.88 mmHg for SBP and 4.62 ± 1.17 mmHg for DBP. The mean correlation coefficient between measured and predicted values was 0.87 for SBP and 0.86 for DBP. Based on Shapley additive explanation (SHAP) values, the most important PPG waveform feature was the stiffness index, a marker that reflects the change in arterial stiffness. Conclusion: Our results highlight the potential of machine learning-based nocturnal BP prediction using single-channel fingertip PPG in healthy adults. The accuracy of the predictions demonstrated that our cuffless method was able to capture the dynamic and complex relationship between PPG waveform characteristics and BP during sleep, which may provide a scalable, convenient, economical, and non-invasive means to continuously monitor blood pressure.
MeSH term(s)	Adult ; Female ; Humans ; Male ; Middle Aged ; Blood Pressure ; Blood Pressure Monitoring, Ambulatory ; Cardiovascular Diseases ; Hypertension ; Machine Learning ; Plethysmography ; Sleep ; Young Adult
Language	English
Publishing date	2023-09-16
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23187931
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