Towards metabolic disconnection – symptom mapping

This scientific commentary refers to ‘Metabolic lesion-deficit mapping of human cognition’ by Jha etal

White matter microstructure of attentional networks predicts attention and consciousness functional interactions

Attention is considered as one of the pre-requisites of conscious perception. Phasic alerting and exogenous orienting improve conscious perception of near-threshold information through segregated brain networks. Using a multimodal neuroimaging approach, combining data from functional MRI (fMRI) and diffusion-weighted imaging (DWI), we investigated the influence of white matter properties of the ventral branch of superior longitudinal fasciculus (SLF III) in functional interactions between attentional systems and conscious perception. Results revealed that (1) reduced integrity of the left hemisphere SLF III was predictive of the neural interactions observed between exogenous orienting and conscious perception, and (2) increased integrity of the left hemisphere SLF III was predictive of the neural interactions observed between phasic alerting and conscious perception. Our results combining fMRI and DWI data demonstrate that structural properties of the white matter organization determine attentional modulations over conscious perception.ABC was supported by a Ramón y Cajal fellowship (RYC-2011-09320) and research project PSI2014-58681-P from the Spanish Ministry of Economy and Competitiveness (MINECO). PMP-A was supported by a Ramón y Cajal fellowship (RYC-2014-15440), and grants PSI2015-65696 and SEV-2015-049 from the MINECO. MTdS received funding from the ‘Agence Nationale de la Recherche’ (Grant number ANR-13-JSV4-0001-01) and “Investissements d’avenir” ANR-10-IAIHU-06. PB received funding from the ‘Agence Nationale de la Recherche’ (Grant number R16139DD)

Post-stroke deficit prediction from lesion and indirect structural and functional disconnection

Behavioural deficits in stroke reflect both structural damage at the site of injury, and widespread network dysfunction caused by structural, functional, and metabolic disconnection. Two recent methods allow for the estimation of structural and functional disconnection from clinical structural imaging. This is achieved by embedding a patient's lesion into an atlas of functional and structural connections in healthy subjects, and deriving the ensemble of structural and functional connections that pass through the lesion, thus indirectly estimating its impact on the whole brain connectome. This indirect assessment of network dysfunction is more readily available than direct measures of functional and structural connectivity obtained with functional and diffusion MRI, respectively, and it is in theory applicable to a wide variety of disorders. To validate the clinical relevance of these methods, we quantified the prediction of behavioural deficits in a prospective cohort of 132 first-time stroke patients studied at 2 weeks post-injury (mean age 52.8 years, range 22-77; 63 females; 64 right hemispheres). Specifically, we used multivariate ridge regression to relate deficits in multiple functional domains (left and right visual, left and right motor, language, spatial attention, spatial and verbal memory) with the pattern of lesion and indirect structural or functional disconnection. In a subgroup of patients, we also measured direct alterations of functional connectivity with resting-state functional MRI. Both lesion and indirect structural disconnection maps were predictive of behavioural impairment in all domains (0.16 < R2 < 0.58) except for verbal memory (0.05 < R2 < 0.06). Prediction from indirect functional disconnection was scarce or negligible (0.01 < R2 < 0.18) except for the right visual field deficits (R2 = 0.38), even though multivariate maps were anatomically plausible in all domains. Prediction from direct measures of functional MRI functional connectivity in a subset of patients was clearly superior to indirect functional disconnection. In conclusion, the indirect estimation of structural connectivity damage successfully predicted behavioural deficits post-stroke to a level comparable to lesion information. However, indirect estimation of functional disconnection did not predict behavioural deficits, nor was a substitute for direct functional connectivity measurements, especially for cognitive disorders

An ancestral anatomical and spatial bias for visually guided behavior

Human behavioral asymmetries are commonly studied in the context of structural cortical and connectional asymmetries. Within this framework, Sreenivasan and Sridharan (1) provide intriguing evidence of a relationship between visual asymmetries and the lateralization of superior colliculi connections—a phylogenetically older mesencephalic structure. Specifically, response facilitation for cued locations (i.e., choice bias) in the contralateral hemifield was associated with differences in the connectivity of the superior colliculus. Given that the superior colliculus has a structural homolog—the optic tectum—which can be traced across all Vertebrata, these results may have meaningful evolutionary ramifications

Neural dissociation of visual attention span and phonological deficits in developmental dyslexia: A hub‐based white matter network analysis

It has been suggested that developmental dyslexia may have two dissociable causes—a phonological deficit and a visual attention span (VAS) deficit. Yet, neural evidence for such a dissociation is still lacking. This study adopted a data-driven approach to white matter network analysis to explore hubs and hub-related networks corresponding to VAS and phonological accuracy in a group of French dyslexic children aged from 9 to 14 years. A double dissociation in brain-behavior relations was observed. Structural connectivity of the occipital-parietal network surrounding the left superior occipital gyrus hub accounted for individual differences in dyslexic children's VAS, but not in phonological processing accuracy. In contrast, structural connectivity of two networks: the temporal–parietal-occipital network surrounding the left middle temporal gyrus hub and the frontal network surrounding the left medial orbital superior frontal gyrus hub, accounted for individual differences in dyslexic children's phonological processing accuracy, but not in VAS. Our findings provide evidence in favor of distinct neural circuits corresponding to VAS and phonological deficits in developmental dyslexia. The study points to connectivity-constrained white matter subnetwork dysfunction as a key principle for understanding individual differences of cognitive deficits in developmental dyslexia

The scientific value of tractography: Accuracy vs usefulness

Tractography has emerged as a central tool for mapping the cerebral white matter architecture. However, its scientific value continues to be a subject of debate, given its inherent limitations in anatomical accuracy. This concise communication showcases key points of a debate held at the 2024 Tract-Anat Retreat, addressing the trade-offs between the accuracy and utility of tractography. While tractography remains constrained by limitations related to resolution, sensitivity, and validation, its usefulness and utility in areas such as surgical planning, disorder prediction, and the elucidation of brain development are emphasized. These perspectives highlight the necessity of context-specific interpretation, anatomically informed algorithms, and the continuous refinement of tractography workflows to achieve an optimal balance between accuracy and utility

Integrating brain function and structure in the study of the human attentional networks: a functionnectome study

Published on 6 July 2024Attention is a heterogeneous function theoretically divided into different systems. While functional magnetic resonance imaging (fMRI) has extensively characterized their functioning, the role of white matter in cognitive function has gained recent interest due to diffusion-weighted imaging advancements. However, most evidence relies on correlations between white matter properties and behavioral or cognitive measures. This study used a new method that combines the signal from distant voxels of fMRI images using the probability of structural connection given by high-resolution normative tractography. We analyzed three fMRI datasets with a visual perceptual task and three attentional manipulations: phasic alerting, spatial orienting, and executive attention. The phasic alerting network engaged temporal areas and their communication with frontal and parietal regions, with left hemisphere dominance. The orienting network involved bilateral fronto-parietal and midline regions communicating by association tracts and interhemispheric fibers. The executive attention network engaged a broad set of brain regions and white matter tracts connecting them, with a particular involvement of frontal areas and their connections with the rest of the brain. These results partially confirm and extend previous knowledge on the neural substrates of the attentional system, offering a more comprehensive understanding through the integration of structure and function.M.M-S. is supported by a Margarita Salas fellowship by the Spanish Ministry of Universities and the European Next Generation funding, and by a contract for Young Researchers (PAIDI 20200) by the Ministry of Economy, Knowledge, Enterprise, and Universities of Andalusia. P.M.P-A. is supported by grants from the Spanish Ministry of Science and Innovation (PID2021-123574NB-I00), from the Basque Government (PIBA-2021-1-0003), from the Red guipuzcoana de Ciencia, Tecnología e Innovación of the Diputación Foral de Gipuzkoa (FA/OF 422/2022), from “la Caixa” Foundation (ID 100010434) under the agreement HR18-00178-DYSTHAL. BCBL acknowledges funding from the Basque Government through the BERC 2022–2025 program and by the Spanish State Research Agency through BCBL Severo Ochoa excellence accreditation CEX2020-001010-S. M.T.D.S is supported by the European Union’s Horizon 2020 research and innovation programme under the European Research Council (ERC) Consolidator grant agreement No. 818521 (M.T.d.S., DISCONNECTOME) M.T.D.S acknowledges funding from the University of Bordeaux’s IdEx ‘Investments for the Future’ program RRI ‘IMPACT’, which received financial support from the French government and IHU ‘Precision & Global Vascular Brain Health Institute – VBHI’ funded by the France 2030 initiative. A.B.C is supported by grants PSI2017-88136 and PID2020-119033 GB-I00 funded by MCIN/AEI/ https://doi.org/10.13039/501100011033, and by “ERDF A way of making Europe”, by the “European Union”; and by FEDER/Junta de Andalucía-Consejería de Economía y Conocimiento/ project A.SEJ.090. UGR18

Testing the disconnectome symptom discoverer model on out-of-sample post-stroke language outcomes

Stroke is common, and its consequent brain damage can cause various cognitive impairments. Associations between where and how much brain lesion damage a patient has suffered, and the particular impairments that injury has caused (lesion-symptom associations) offer potentially compelling insights into how the brain implements cognition.1 A better understanding of those associations can also fill a gap in current stroke medicine by helping us to predict how individual patients might recover from post-stroke impairments.2 Most recent work in this area employs machine learning models trained with data from stroke patients whose mid-to-long-term outcomes are known.2-4 These machine learning models are tested by predicting new outcomes—typically scores on standardized tests of post-stroke impairment—for patients whose data were not used to train the model. Traditionally, these validation results have been shared in peer-reviewed publications describing the model and its training. But recently, and for the first time in this field (as far as we know), one of these pre-trained models has been made public—The Disconnectome Symptom Discoverer model (DSD) which draws its predictors from structural disconnection information inferred from stroke patients’ brain MRI.5Here, we test the DSD model on wholly independent data, never seen by the model authors, before they published it. Specifically, we test whether its predictive performance is just as accurate as (i.e. not significantly worse than) that reported in the original (Washington University) dataset, when predicting new patients’ outcomes at a similar time post-stroke (∼1 year post-stroke) and also in another independent sample tested later (5+ years) post-stroke. A failure to generalize the DSD model occurs if it performs significantly better in the Washington data than in our data from patients tested at a similar time point (∼1 year post-stroke). In addition, a significant decrease in predictive performance for the more chronic sample would be evidence that lesion-symptom associations differ at ∼1 year post-stroke and >5 years post-stroke