Resting connectivity predicts task activation in pre-surgical populations
Injury and disease affect neural processing and increase individual variations in patients when compared with healthy controls. Understanding this increased variability is critical for identifying the anatomical location of eloquent brain areas for pre-surgical planning. Here we show that precise an...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2017-01-01
|
| Series: | NeuroImage: Clinical |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158216302650 |
| _version_ | 1818542364280487936 |
|---|---|
| author | O. Parker Jones N.L. Voets J.E. Adcock R. Stacey S. Jbabdi |
| author_facet | O. Parker Jones N.L. Voets J.E. Adcock R. Stacey S. Jbabdi |
| author_sort | O. Parker Jones |
| collection | DOAJ |
| description | Injury and disease affect neural processing and increase individual variations in patients when compared with healthy controls. Understanding this increased variability is critical for identifying the anatomical location of eloquent brain areas for pre-surgical planning. Here we show that precise and reliable language maps can be inferred in patient populations from resting scans of idle brain activity. We trained a predictive model on pairs of resting-state and task-evoked data and tested it to predict activation of unseen patients and healthy controls based on their resting-state data alone. A well-validated language task (category fluency) was used in acquiring the task-evoked fMRI data. Although patients showed greater variation in their actual language maps, our models successfully learned variations in both patient and control responses from the individual resting-connectivity features. Importantly, we further demonstrate that a model trained exclusively on the more-homogenous control group can be used to predict task activations in patients. These results are the first to show that resting connectivity robustly predicts individual differences in neural response in cases of pathological variability. |
| first_indexed | 2024-12-11T22:21:06Z |
| format | Article |
| id | doaj.art-dc8083ee053d410f9ec3b0656c50f293 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-11T22:21:06Z |
| publishDate | 2017-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-dc8083ee053d410f9ec3b0656c50f2932022-12-22T00:48:26ZengElsevierNeuroImage: Clinical2213-15822017-01-0113C37838510.1016/j.nicl.2016.12.028Resting connectivity predicts task activation in pre-surgical populationsO. Parker Jones0N.L. Voets1J.E. Adcock2R. Stacey3S. Jbabdi4FMRIB Centre, NDCN, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UKFMRIB Centre, NDCN, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UKOxford Epilepsy Research Group, NDCN, University of Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UKDepartment of Neurosurgery, Oxford University Hospitals NHS Trust, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UKFMRIB Centre, NDCN, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DU, UKInjury and disease affect neural processing and increase individual variations in patients when compared with healthy controls. Understanding this increased variability is critical for identifying the anatomical location of eloquent brain areas for pre-surgical planning. Here we show that precise and reliable language maps can be inferred in patient populations from resting scans of idle brain activity. We trained a predictive model on pairs of resting-state and task-evoked data and tested it to predict activation of unseen patients and healthy controls based on their resting-state data alone. A well-validated language task (category fluency) was used in acquiring the task-evoked fMRI data. Although patients showed greater variation in their actual language maps, our models successfully learned variations in both patient and control responses from the individual resting-connectivity features. Importantly, we further demonstrate that a model trained exclusively on the more-homogenous control group can be used to predict task activations in patients. These results are the first to show that resting connectivity robustly predicts individual differences in neural response in cases of pathological variability.http://www.sciencedirect.com/science/article/pii/S2213158216302650ConnectivityIndividual variationNeural pathologyResting-state fMRI |
| spellingShingle | O. Parker Jones N.L. Voets J.E. Adcock R. Stacey S. Jbabdi Resting connectivity predicts task activation in pre-surgical populations NeuroImage: Clinical Connectivity Individual variation Neural pathology Resting-state fMRI |
| title | Resting connectivity predicts task activation in pre-surgical populations |
| title_full | Resting connectivity predicts task activation in pre-surgical populations |
| title_fullStr | Resting connectivity predicts task activation in pre-surgical populations |
| title_full_unstemmed | Resting connectivity predicts task activation in pre-surgical populations |
| title_short | Resting connectivity predicts task activation in pre-surgical populations |
| title_sort | resting connectivity predicts task activation in pre surgical populations |
| topic | Connectivity Individual variation Neural pathology Resting-state fMRI |
| url | http://www.sciencedirect.com/science/article/pii/S2213158216302650 |
| work_keys_str_mv | AT oparkerjones restingconnectivitypredictstaskactivationinpresurgicalpopulations AT nlvoets restingconnectivitypredictstaskactivationinpresurgicalpopulations AT jeadcock restingconnectivitypredictstaskactivationinpresurgicalpopulations AT rstacey restingconnectivitypredictstaskactivationinpresurgicalpopulations AT sjbabdi restingconnectivitypredictstaskactivationinpresurgicalpopulations |