So is this where we lose 5 cognitive years from the stroke?
The white matter damage? How EXACTLY is your doctor recovering from that damage? Or is nothing being done? My doctor knew nothing and did nothing for any piece of my recovery. It was all from my excellent OT who supposedly used Bobath/NDT. My ER doctors blew out my clot in 90 minutes so I only lost 171 million neurons, since my PMR doctor did nothing in the first week, that caused another 5.4 billion neurons to die. I will never forgive him for that, especially since for decades nobody even considered doing any analysis as to why survivors didn't recover. In programming I would have been fired in no time for not solving the root cause of the problem I was assigned.
A neuromarker of individual general fluid intelligence from the white-matter functional connectome
Abstract
Neuroimaging studies have uncovered the neural roots of individual differences in human general fluid intelligence (Gf). Gf is characterized by the function of specific neural circuits in brain gray-matter; however, the association between Gf
and neural function in brain white-matter (WM) remains unclear. Given
reliable detection of blood-oxygen-level-dependent functional magnetic
resonance imaging (BOLD-fMRI) signals in WM, we used a functional,
rather than an anatomical, neuromarker in WM to identify individual Gf.
We collected longitudinal BOLD-fMRI data (in total three times, ~11
months between time 1 and time 2, and ~29 months between time 1 and time
3) in normal volunteers at rest, and identified WM functional
connectomes that predicted the individual Gf at time 1 (n = 326).
From internal validation analyses, we demonstrated that the constructed
predictive model at time 1 predicted an individual’s Gf from WM functional connectomes at time 2 (time 1 ∩ time 2: n = 105) and further at time 3 (time 1 ∩ time 3: n = 83).
From external validation analyses, we demonstrated that the predictive
model from time 1 was generalized to unseen individuals from another
center (n = 53). From anatomical aspects, WM functional
connectivity showing high predictive power predominantly included the
superior longitudinal fasciculus system, deep frontal WM, and ventral
frontoparietal tracts. These results thus demonstrated that WM
functional connectomes offer a novel applicable neuromarker of Gf and supplement the gray-matter connectomes to explore brain–behavior relationships.
Introduction
Neuroimaging
and psychological studies have investigated the neural basis of the
cognitive processes that motivate novel insights about brain–behavior
relationships1. An enduring aim of brain and cognitive sciences is to understand individual differences in human intelligence2. Human general fluid intelligence (Gf) refers to an ability to think logically and to solve novel problems that do not rely on previously acquired knowledge3,4. Gf
has been broadly quantified using a series psychometric test, which
further provides a foundation to process brain–behavior associations5,6. Given that individual differences are inherent to Gf, it is crucial to identify neural correlates of Gf and corresponding variations in brain structure and function.
The neural correlates of individual differences in Gf may be associated with variations in brain size and connections2. A larger brain size (volume) consistently indicates higher intelligence7; this concept of “the bigger brain, the better intelligence” may result from the efficiency of information flow among neurons5,8. Recently, the information flow among certain areas associated with Gf have been quantified by functional connectivity studies. These results showed that the variational relationships of regions engaging in common or related performance (even at rest) may be the basis of individual differences in Gf9,10,11, and measurements of the activity of the resting-state human brain might carry information about intelligence12. Furthermore, the rate of information flow among the distributed parietal and frontal areas composing the parieto-frontal integration theory (P-FIT) network are likely to play key roles in intelligence13. It is not surprising that a large number of these brain regions and brain functional networks are related to individual Gf12,14,15, due to the diverse abilities associated with Gf, including understanding of daily tasks and problem solving. Accordingly, whole-brain functional connectivity measures may provide more holistic insight to determine an individual’s Gf, rather than global brain size.
Gf is mainly rooted in the functional connectivity of specific neural circuits within gray-matter (GM); however, little is known regarding the neurofunctional substrates of individual differences in Gf in white-matter (WM)16,17,18. Recently, a small but increasing number of investigations have demonstrated a reliable detection of blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI) signals in WM. These studies indicate that neural activation elicits temporal and spectral profiles of hemodynamic responses in WM that are similar to those measured in GM during different functional tasks19,20,21,22,23,24. In parallel with the detection of task-related activations, BOLD-fMRI can also reflect the neural activity in WM at rest (i.e., absence of task requirement)25,26. Specifically, we found that, during the resting state, the power of low-frequency BOLD-fMRI fluctuations in WM exhibited a specific rather than a random distribution of noise26, and the WM functional connectome exhibited reliable and stable small-worldness and nonrandom modularity27. Abnormal small-worldness in the WM functional connectome was reported in patients with Parkinson’s disease28. Furthermore, the specific functional connectivity organization of the anatomical bundles was able to be identified by resting-state fMRI20,21,25,29. These investigations not only provided evidence of neural activity and connectivity, using fMRI, but also established cognitive biomarkers (i.e., for memory function) in WM30.
In the current study, using a cross-validation, data-driven analysis, we present novel findings that the whole-brain WM functional network predicted individual Gf and shed light on the possible neurofunctional correlates of Gf in WM. We first built a network-predicted model between connectivity strength and Gf scores of normal individuals following an initial examination (time 1, n = 326 participants). We demonstrated that the network-predicted model derived from these data could predict an individual’s Gf from his/her WM functional connectivity. This predictive model constructed at time 1 can be generalized to both the second (time 2, n = 105 participants) and the third examinations (time 3, n = 83 participants) using the overlapped individuals, thus, accurately predicting an individual’s Gf score from WM functional connectivity during the time 2 and time 3 scans for internal validation. Finally, to further test the generalizability of the predicted model, we showed that this model could also predict novel independent performance Gf (n = 53 participants) for external validation. These results suggested that the whole-brain functional connectivity of WM was a neuromarker of individual differences in Gf and would generalize to independent data to predict individual Gf.
The neural correlates of individual differences in Gf may be associated with variations in brain size and connections2. A larger brain size (volume) consistently indicates higher intelligence7; this concept of “the bigger brain, the better intelligence” may result from the efficiency of information flow among neurons5,8. Recently, the information flow among certain areas associated with Gf have been quantified by functional connectivity studies. These results showed that the variational relationships of regions engaging in common or related performance (even at rest) may be the basis of individual differences in Gf9,10,11, and measurements of the activity of the resting-state human brain might carry information about intelligence12. Furthermore, the rate of information flow among the distributed parietal and frontal areas composing the parieto-frontal integration theory (P-FIT) network are likely to play key roles in intelligence13. It is not surprising that a large number of these brain regions and brain functional networks are related to individual Gf12,14,15, due to the diverse abilities associated with Gf, including understanding of daily tasks and problem solving. Accordingly, whole-brain functional connectivity measures may provide more holistic insight to determine an individual’s Gf, rather than global brain size.
Gf is mainly rooted in the functional connectivity of specific neural circuits within gray-matter (GM); however, little is known regarding the neurofunctional substrates of individual differences in Gf in white-matter (WM)16,17,18. Recently, a small but increasing number of investigations have demonstrated a reliable detection of blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI) signals in WM. These studies indicate that neural activation elicits temporal and spectral profiles of hemodynamic responses in WM that are similar to those measured in GM during different functional tasks19,20,21,22,23,24. In parallel with the detection of task-related activations, BOLD-fMRI can also reflect the neural activity in WM at rest (i.e., absence of task requirement)25,26. Specifically, we found that, during the resting state, the power of low-frequency BOLD-fMRI fluctuations in WM exhibited a specific rather than a random distribution of noise26, and the WM functional connectome exhibited reliable and stable small-worldness and nonrandom modularity27. Abnormal small-worldness in the WM functional connectome was reported in patients with Parkinson’s disease28. Furthermore, the specific functional connectivity organization of the anatomical bundles was able to be identified by resting-state fMRI20,21,25,29. These investigations not only provided evidence of neural activity and connectivity, using fMRI, but also established cognitive biomarkers (i.e., for memory function) in WM30.
In the current study, using a cross-validation, data-driven analysis, we present novel findings that the whole-brain WM functional network predicted individual Gf and shed light on the possible neurofunctional correlates of Gf in WM. We first built a network-predicted model between connectivity strength and Gf scores of normal individuals following an initial examination (time 1, n = 326 participants). We demonstrated that the network-predicted model derived from these data could predict an individual’s Gf from his/her WM functional connectivity. This predictive model constructed at time 1 can be generalized to both the second (time 2, n = 105 participants) and the third examinations (time 3, n = 83 participants) using the overlapped individuals, thus, accurately predicting an individual’s Gf score from WM functional connectivity during the time 2 and time 3 scans for internal validation. Finally, to further test the generalizability of the predicted model, we showed that this model could also predict novel independent performance Gf (n = 53 participants) for external validation. These results suggested that the whole-brain functional connectivity of WM was a neuromarker of individual differences in Gf and would generalize to independent data to predict individual Gf.
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