Advanced Methods for Connectome-Based Predictive Modeling of Human Intelligence: A Novel Approach Based on Individual Differences in Cortical Topography
Abstract
Individual differences in human intelligence can be modeled and predicted fromin vivo neurobiological connectivity.
Many established modeling frameworks forpredicting intelligence, however, discard higher-order information about individual differences in brain network topology, and show only moderate performancewhen generalized to make predictions in out-of-sample subjects.
In this paper,we propose that connectome-based predictive modeling, a common predictivemodeling framework for neuroscience data, can be productively modiﬁed to incorporate information about brain network topology and individual differences via theincorporation of bagged decision trees and the network based statistic.
These modiﬁcations produce a novel predictive modeling framework that leverages individualdifferences in cortical tractography to generate accurate regression predictionsof intelligence.
Network topology-based feature selection provides for nativelyinterpretable networks as input features, increasing the model’s explainability. Investigating the proposed modeling framework’s efﬁcacy, we ﬁnd that advancedconnectome-based predictive modeling generates neuroscience predictions thataccount for a signiﬁcantly greater proportion of variance in intelligence than previously established methods, advancing our scientiﬁc understanding of the networkarchitecture that underlies human intelligence.
Predicting Individual Differences in General Intelligence
Cognitive neuroscience research has begun to turn greater attention to individual differences in neurobiology and cognition (1), motivating the development of research methods that more directly addressindividual differences in neurosceicne data.
Integrating cognitive neuroscience and computer sciencemethods and perspectives together promises to advance the explanability and replicability of scientiﬁcresults (2) and will be of particular importance for advancing research into the neruosceince ofinter-individual differences (3).
In this paper, we propose a novel predictive modeling framework thatmodiﬁes connectome-based predictive modeling (CPM) (4; 5), an existing computational approachfor predicting behavioral data from neuroscience data and demonstrate the method’s efﬁcacy for predicting intelligence using individual differences in cortical topography
1.1 Existing Computational Cognitive Neuroscience Approaches for Predicting Intelligence
Intelligence is a central trait underpinning individual differences in cognitive ability (6).
While theneurobiological basis of intelligence has long been identiﬁed with the architecture of speciﬁc brain networks (7), recent work has begun to establish that that systemwide brain network topology anddynamics are critical sources of the broad individual differences observed in cognitive ability (8).
Several previous methods have been applied to model and predict human intelligence from neuroscience data.
Connectome-based predictive modeling (CPM) has previously been deployed topredict ﬂuid intelligence scores (4), showing that CPM’s predictions could account for 25% of thevariance observed across actual ﬂuid intelligence.
Other approaches, such as cortical hyperalignment,have accounted for up to 39% of the variance in general intelligence on the basis of single brainregion’s connectivity, and on average account for 27% of variance in general intelligence when usingwhole-brain connectivity (9).
The efﬁciency of weakly-connected edges (extracted from windowedfunctional connectivity) have been used in a correlation framework to account for 37.5% of thevariance in IQ (10).
Elastic net models are able to account for 20% of the variance in intelligence,on the basis of distributed network of resting state connections that span the functional connectome(11).
Each method has desirable properties—use of strong connections, use of weak connections,native interpretability, predictive accuracy—that our advanced modeling framework will attempt toincorporate.
Modeling and generating predictions from individual differences in functional connectivity remainsan important goal for neuroscience (12), requiring interpretable AI methods that model individualvariability in neuroscience data (3).
Here, we investigated the feasibility of modifying CPM to createan advanced predictive modeling framework that incorporates information about network topologyand individual differences.
One previously unremarked feature of CPM is that it ablates brain networktopology during feature selection (see (5)).
As neuroscience evidence suggests that individualdifferences in network topology are important for explaining cognitive abilities, we incorporated anatively interpretable network-based feature selection into our advanced CPM to extract individualdifferences in cortical topology.
1.2 Connectome-Based Predictive Modeling
We ﬁrst deployed standard connectome-based predictive modeling to quantify the baseline variance ingeneral intelligence in our sample accounted for by standard CPM (5).
CPM ﬁlters brain regions usingmass-unvariate statistical thresholding against the behavioral outcome, maximizing the proportionof true positives edges included in the model.
These features are then summarized (i.e., added) tocreate a single value per subject, which train a linear model between aggregate neuroscience dataand behavioral outcome.
The procedure is performed under cross-validation to generate behavioralpredictions for each subject.
Our baseline implementation of CPM incorporated edges with bothpositive and negative relationships to intelligence simultaneously, aggregating edges by sign and thencombining those two values to produce a single value per subject.
1.3 Network-Based Statistic
To include interpretable information about network topology, our advanced CPM replaces massunivariate feature selection with a network based statistic (NBS) model (13).
NBS uses permutationtesting to assess the relationship between individual network edges and a behavioral outcome,explicitly considering network topology while deploying an interpretable linear model to identifyedges that form a signiﬁcant and topologically connected network.
Although isolated functionaledges contains information about the BOLD timeseries of two incident regions, the mesoscale andmacroscale topology of the larger networks cannot be easily inferred or reconstructed from manysuch aggregated edge weights.
Deploying NBS to perform feature selection allows our advanced CPM framework to train on features that not only relate signiﬁcantly to intelligence, but also form aconnected network, explicitly including network topology information into the modeling process.
1.4 Bootstrap Aggregation
We further modiﬁed CPM to attend to individual differences by eliminating the feature summarizationstep, instead training with disaggregated features (i.e., topologically connected functional edges)using bagged random forests (14).
Bagged random forests have have several desirable properties,including a low number of model parameters and resistance to overﬁtting in the training population(as individual trees are uncorrelated).
Here, we selected bagged random forests for two primaryreasons.
First, disaggregating edge strengths preserves individual difference information about network topology, and bagged random forests are capable of training on these individual edgesand producing a regression output.
Second, each tree is bootstrapped by oversampling from asubpopulation in the data (ie, bootstrap resampling), allowing for an ensemble of models that aretrained (and overﬁt) with variability unique to subpopulation of individuals.
The resulting decisiontrees do not model variability common to the whole sample, but instead will overﬁt to variance uniqueto the bootstrap replicate, characterizing sources of individual difference unique to that subpopulation.
Aggregating the predictions of many uncorrelated decision trees can lead to good performance forgeneralizing from highly variable data (such as functional connectivity data, see (15)).
1.5 Data Acquisition
We acquired a large ( N = 297 ) dataset of 10-minute resting state EPI scans using 3 Tesla MRI, andprocessed them using reproducible methods for analysis of neuroimaging data (via ICA-AROMAdenoising (16) through FMRIPREP (17) and xcpEngine (18)).
Diffusion tensor imaging data wasalso acquired in N = 288 subjects on the half-shell and processed using FSL’s FDT and bedpostx forprobabalistic diffusion tensor tractography (19).
Imaging data were parcellated using a multimodal360-region atlas of cortical grey matter (20).
Additionally, we administered a comprehensive batteryof neuropsychological tests and deployed structural equation modeling to assess individual differencesin general, ﬂuid, and crystallized intelligence. 1.6 Results and Discussion
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[Figure] [Figure] [Figure] CPM predictive accuracy (ﬁg. 2a) was notably lower than in a previous report (4), possibly explainedby our smaller sample size, our more robust motion artifact removal (21), and the broader set ofcognitive operations entailed by our dependent variable g .
Network-based statistic identiﬁed bothfunctional and structural networks reliably associated with general intelligence (ﬁg. 1), and thesenetwork edges produced reliable N-fold predictions of intelligence when combined with baggedrandom forests, accounting for 63–70% of the variance between subjects (ﬁgs. 2b and 2c).
The rangeof predictions was notably narrow—this may be due in part to the sign consistency of feature-selectededges imposed by NBS (either all positive or all negative) as a consequence of t-thresholding.
CPM trains using edges both positively and inversely associated with intelligence, unlike the individual sign-consistent networks identiﬁed by NBS from DTI and fMRI data.
Finally, to induce thisbidirectional property in advanced CPM, we feature selected the union of network edges identiﬁedfrom fMRI and DTI data.
This multimodal network selection retained weak functional connectionsinversely associated with intelligence, and added functional edges for which underlying white matterconnectivity was positively associated with intelligence.
Incorporating both strong and weak functional edges improved prediction spread and accounted for 76% of the variance in intelligence (ﬁg.2d).
In all cases, variance explained by advanced CPM exceeded previous ﬁndings in the literature(4; 9; 10; 11).
We showed that advanced CPM generates reliable behavioral predictions when trained using individual difference in natively interpretable cortical networks.
Our results suggest that both strong and weak functional edges contain important predictive signals and that multimodal feature selection produces the highest accuracy predictions, suggesting that individual differences in global network topology are critical in producing intelligence.
These ﬁndings motivate future work using Shapley values to more precisely interpret individual differences in topological feature importance
Our ﬁndings highlight the utility of incorporating intrepretable information about network topology into the study of individual differences in cognition and further advance our scientiﬁc understanding of the network architecture underlying human intelligence