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. 2025 Feb 11;45:103753. doi: 10.1016/j.nicl.2025.103753

Correspondence between thalamic injury-induced changes in resting-state fMRI of monkeys and their sensorimotor behaviors and neural activities

Anirban Sengupta a,b,⁎, Pai-Feng Yang a,b, Jamie L Reed a,b, Arabinda Mishra a,b, Feng Wang a,b, Isaac V Manzanera Esteve c, Zhangyan Yang a,d, Li Min Chen a,b,d,1, John C Gore a,b,d,e,1
PMCID: PMC11889736  PMID: 39983550

Highlights

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    Injury to the thalamic VPL exerts a cascading effect to different brain regions.

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    RsfMRI based FC can predict behavioral recovery time following VPL injury.

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    RsfMRI based FC changes agree with LFP changes post VPL injury.

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    RsfMRI based FC changes agree with the post-mortem histology measures.

Keywords: Thalamus, fMRI, Functional Connectivity, VPL

Abstract

Resting state functional MRI (rsfMRI) exploits variations in blood-oxygenation-level-dependent (BOLD) signals to infer resting state functional connectivity (FC) within and between brain networks. However, there have been few reports quantifying and validating the results of rsfMRI analyses with other metrics of brain circuits. We measured longitudinal changes in FC both within and between brain networks in three squirrel monkeys after focal lesions of the thalamic ventroposterior lateral nucleus (VPL) that were intended to disrupt the input to somatosensory cortex and impair manual dexterity. Local field potential signals were recorded to assess electrophysiological changes during each animal’s recovery, and behavioral performances were measured longitudinally using a sugar-pellet grasping task. Finally, end-point histological evaluations were performed on brain tissue slices to quantify the VPL damage. The rsfMRI data analysis showed significant decrease in FC measures both within and between networks immediately post-injury, which started to recover at different time-points for each animal. The trajectories of FC recovery for each animal mirrored their individual behavioral recovery time-courses. Electrophysiological measurements of inter-electrode coherences and end-point histological measures also aligned well with the graded injury effects measured using rsfMRI-based FC. A simple algorithm employing FC measures from the somatosensory network could accurately predict each monkeys’ behavioral recovery timeframe after four weeks post-injury. Whole brain between-network FC measures further revealed that the injury effects were not limited to thalamocortical connections but were rather more widespread. Overall, this study provides evidence of the validity of rsfMRI based FC measures as indicators of the functional integrity and behavioral relevance following an injury to a specific brain circuit.

1. Introduction

Blood-oxygenation-level-dependent (BOLD) functional MRI (fMRI) signals were first reported by Ogawa et al. (Ogawa et al., 1990) and subsequently exploited to detect and locate temporal information on changes in neural activity in the brain elicited by a stimulus or task (Kwong et al., 1992, Xiong et al., 1999). Resting state fMRI (rsfMRI) refers to the repeated acquisition of BOLD signals in a task-free state in the absence of external stimuli or task. Temporal correlations between such spontaneously occurring BOLD signals at low frequencies (0.01–0.1 Hz) were first reported in the motor cortex of human brain by Biswal et al (Biswal et al., 1995) and have been interpreted as revealing functional connectivity between regions. Since then, rsfMRI based correlations have become widely used for mapping intrinsic functional networks within different regions of the resting brain (Damoiseaux et al., 2006, Hampson et al., 2002, Smith et al., 2013, van den Heuvel and Hulshoff Pol, 2010) and have been used to investigate how these networks change in various disorders including Alzheimer’s disease (Lustig et al., 2003), autism (Kennedy et al., 2006), depression (Anand et al., 2005), multiple sclerosis (Lowe et al., 2002), and drug addiction (Lerman et al., 2014, Menon, 2011, Sutherland et al., 2012). Resting state functional connectivity (FC) measurements are based on the identification of significant temporal correlations of spontaneous low-frequency BOLD signals - within and between different brain regions during rest. Despite the large number of reports of the use of FC measurements, there have been few studies that validate their interpretation as metrics of functional connectivity, or as reliable indicators of changes in brain networks after an injury especially in a longitudinal study. Below we report studies on an animal model before and after injury, longitudinally over time, that attempt to add to our confidence in the interpretation of rsfMRI correlations.

Squirrel monkeys (Saimiri sciureus) are a species of considerable interest in neuroscientific research due to their neuroanatomical and behavioral similarities to humans. In a recent report we detected 15 anatomically constrained resting state networks (RSNs) within the brain of anesthetized squirrel monkeys, encompassing both cortical and subcortical regions (Sengupta et al., 2023) which provided a functional atlas of the squirrel monkey brain for comparisons with other studies. These 15 networks were consistent with previously reported networks in humans (Beckmann et al., 2005, Damoiseaux et al., 2006, Smith et al., 2009) and other non-human primates such as macaques (Hutchison et al., 2011) and marmosets (Belcher et al., 2013) and were derived using data-driven Independent Component Analysis (ICA). Here we build on this previous study to measure the injury effects resulting from selectively disrupting thalamic inputs to the cortex through targeted unilateral lesions of the ventroposterior lateral (VPL) nucleus of the thalamus. Despite previous research on rats (Aggleton et al., 1996, Ndode-Ekane et al., 2021), monkeys (Bornschlegl and Asanuma, 1987, Parker and Gaffan, 1997), and humans (Banks et al., 2016, Messé et al., 2013, Sours et al., 2015, Tang et al., 2011, Woodrow et al., 2023) there remains a paucity of literature investigating detailed alterations in cortical and sub-cortical functional networks following a thalamic lesion. We therefore aimed to longitudinally measure the effects of a thalamic lesion using rsfMRI and determine the alterations in FC that occur within and between brain networks.

The thalamic VPL nucleus processes and relays sensory information from the body to the somatosensory cortex, where further integration and processing occur. This relay of sensory information is essential for the perception of touch, pressure, vibration, proprioception (awareness of body position and movement), and pain sensations. The first-order thalamic nuclei, such as VPL, also receive feedback from the cortex. Notably, higher-order thalamic nuclei receive dense cortical projections as part of cortico-thalamo-cortical pathways, which result in indirect connections between distinct cortical areas (Saalmann, 2014, Sherman and Guillery, 2013, Sherman and Guillery, 2005, Sherman and Guillery, 2002). Therefore, the thalamocortical system plays a critical role in integrating information across multifunctional pathways. We introduced a targeted unilateral lesion in the hand region within the VPL nucleus to modulate manual dexterity of one hand as a behavioral output that would reflect the integrity of relevant functional networks. This study provides a useful testbed for demonstrating whether rsfMRI based measurements of FC can detect and circumscribe the consequences of such injuries by investigating whole brain functional circuits.

A second aim was to correlate the inter-animal differences in rsfMRI measures with their longitudinal behavioral performances, electrophysiology measures over time, and histology measures at the end. Previous research has established a clear relationship between neural activity and BOLD signals in specific brain regions during stimulation and task-based conditions (Biswal et al., 1995, Smith et al., 2013). However, direct comparisons between local field potentials (LFPs) and rsfMRI signals, particularly in the brain post-injury, are underexplored (Logothetis et al., 2001, Wu et al., 2019a). Here we recorded LFPs from regions of somatosensory cortex at different times post-injury to identify if changes in coherence in the animals correspond to their changes in FC measures obtained from rsfMRI. In addition, the practical value of FC measurements relies on their ability to be behaviorally relevant and vary in parallel with measures of performance of specific functional tasks. Thalamic VPL injuries often manifest with deficits in sensorimotor functions, particularly impacting skilled hand use (Bornschlegl and Asanuma, 1987, Krause et al., 2012, Montes et al., 2005, Nagasaka et al., 2017, Sprenger et al., 2012). Behavioral assessments of skilled hand use, such as successful food grasping, serve as valuable indicator of sensory deficits and subsequent recovery trajectories (Qi et al., 2013). We quantified the monkey’s performance on a sugar pellet grasping task to assess the behavioral deficits and recovery process following the thalamic VPL injury. Furthermore, we explored the potential use of FC metrics to predict the behavioral outcomes after injury. Across animals and over time, performance on the task varied so we could evaluate whether FC and other measurements corresponded to these changes. Finally, employing an animal model allows for histological evaluation to assess the extent of tissue injury. We quantified the thalamus tissue damage and compared the locations and extents of injury with the FC measures to further validate the interpretation of rsfMRI as a biomarker of injury effects.

The results described below show that a targeted injury to VPL in squirrel monkeys altered the FC of both local and global brain networks including cortico-cortical and cortico-subcortical circuits. The FC measures derived from rsfMRI of somatosensory network change in a behaviorally relevant manner post injury. Also, the FC changes corresponded well with the changes in the coherence of local field potentials (LFPs) within the somatosensory region. Further, we demonstrate that injury effects inferred from FC measures agree with measures of thalamus VPL damage confirmed by post-mortem histopathology. Together, our findings demonstrate the correspondence of rsfMRI based FC changes with other modalities of investigation and establish its utility as a biomarker of injury effects after a thalamus lesion.

2. Materials and methods

2.1. MRI data acquisition

Three adult, male squirrel monkeys (Saimiri sciureus) were each scanned before, and 3–4 times within the first 9 weeks after a targeted injury to the thalamus. The mean ± s.d age of the monkeys was 3 years 10 months ± 7 months at the time of injury. The mean ± s.d weight preinjury was 817 ± 40 gm. All MRI scans were performed on a 9.4 T Varian/Agilent MRI scanner using a quadrature birdcage volume coil (inner diameter 85 mm). High-resolution T2*-weighted anatomical images were acquired on 24 contiguous axial slices covering the whole brain (TR (repetition time)/TE (echo time): 480/10 ms, 0.125 × 0.125 mm2 in-plane resolution, 512 × 512 matrix, slice thickness 1 mm). FMRI data were collected for the same slices using a two-shot gradient echo planar imaging (GE-EPI) sequence (repetition time TR = 1500 ms, echo time TE = 16 ms, resolution 1 × 1 × 1 mm3, 3 s per volume, interleaved slices). In each MRI session, multiple resting state fMRI runs (300/210 volumes per run, 3–4 runs each session) were acquired prior to recording stimulation runs (150 volumes per run, 3–6 runs each session). The animal preparation during scanning is described in the Supplementary Information (SI).

All procedures were performed under an animal protocol approved by the Institutional Animal Care and Use Committee (IACUC) of Vanderbilt University and followed the Public Health Service policy on humane care and use of laboratory animals. Vanderbilt is an accredited institution by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) program.

2.2. Thalamus lesion

Initially electrophysiological receptive field mapping was performed to define the hand representation within the VPL nucleus in each animal based on stereotaxic coordinates (Gergen and MacLean, 1962). Next, lesions were targeted at the predefined thalamic VPL nucleus by a combination of electrolytic and chemical means. Anodal and cathodal electric current pulses (of 0.8 mA intensity, 6 s each) were delivered via elgiloy alloy microelectrodes (Koyano et al., 2011) to predefined penetration sites intended to cover the entire hand representation. Supplemental administration of ibotenic acid, recognized for its neurotoxic properties, was also injected at a concentration of 15 ug/uL in 0.1 M phosphate-buffered saline (PBS) in 1uL volumes across multiple penetration sites following published procedures (Murata et al., 2008). Closing procedures and post-operative animal care were performed as previously described (Qi et al., 2011, Wu et al., 2016).

2.3. FMRI data pre-processing and analysis

The rsfMRI data underwent slice-by-slice, 2D motion correction implemented in MATLAB R2020b. Functional image volumes were first aligned using a 2D rigid body motion correction algorithm based on maximization of mutual information, by which three motion parameters were estimated (two translations and one rotation) (Mishra et al., 2019). Motion parameters (two translations and one rotation), along with temporal signals extracted from CSF regions containing at least 70 % of the cumulative variance (using principal components analysis) were considered as nuisance parameters. They were regressed out using a general linear model (Barry et al., 2018, Barry et al., 2017, Chen et al., 2015, Wu et al., 2019b). No spatial smoothing or global signal regression were performed.

The fMRI signals were then corrected for physiological noise (respiratory and cardiac signal) using RETROICOR (Glover et al., 2000). The axial fMRI images were up sampled from 1.00 mm2 to 0.125 mm2 resolution to match the anatomic images. The rsfMRI signals were band-pass filtered (Chebyshev type2 IIR filter, cut-off frequencies 0.01 and 0.1 Hz) prior to functional connectivity analyses. For group level analysis we used the SM brain atlas (VALiDATe29 Atlas) (Schilling et al., 2017) as the template for registration.

2.4. Independent component analysis (ICA) of resting state fMRI data

We used the results from our recently published study in which we detected 15 anatomically constrained whole brain resting-state networks of squirrel monkey brain using ICA of resting state fMRI data of 14 monkeys (Sengupta et al., 2023). The cortical networks encompassed the visual, somatosensory, executive control, sensorimotor, salience and default mode regions, and the subcortical networks included the hippocampus-amygdala, thalamus, basal-ganglia and brainstem regions. Most importantly these networks bear high correspondence with networks previously observed in humans (Beckmann et al., 2005, Damoiseaux et al., 2006, Smith et al., 2009) and those reported in other NHP species such as macaques (Hutchison et al., 2011) and marmosets (Belcher et al., 2013).

2.5. Functional connectivity measures

We computed two measures of connectivity using our previous ICA networks: Within Network Connectivity (WNC) and Between Network Connectivity (BNC). WNC was computed by measuring the correlation (using Pearson’s correlation coefficient r) between the time-courses of the voxels within an ICA component and taking their mean absolute value. Each ICA component comprises a group of voxels which shows similar time courses of their fluctuations of low-frequency signals during a resting state, so the WNC measures the strength of their coherence. In particular, we computed WNC for one of the somatosensory networks detected in our previous study represented as Independent Component 1 or IC1. This component includes the primary somatosensory cortex (S1, containing areas 1, 2, 3a and 3b) so an injury to the thalamic VPL nucleus may directly affect the functional integrity of this region. WNC was computed for each of the 3 monkeys before the injury and at 3–4 times post injury. We divided the post-injury period into 3-stages: Stage1: 1/2 weeks, Stage2: 4/5 weeks, and Stage3: 8/9 weeks to compare between the monkeys.

BNC was computed between all the 15 ICs detected using ICA from our previous reported study (Sengupta et al., 2023). The mean time-courses from the voxels in each network were correlated (Pearson’s correlation coefficient r) with the mean time-courses obtained from other networks. The BNC measures obtained from IC1 as well as all the I5 ICs combined were compared between different time points pre- and post-injury using statistical tests described in the statistical analysis subsection. We also compared the post-injury WNC and BNC values from IC1 of these 3 monkeys with the pre-lesion or baseline measures obtained from rsfMRI of 14 normal monkeys from our previous published study.

In order to confirm the results from ICA based FC analysis, we also performed the traditional approach of seed-based FC analysis. A small ROI (5–8 voxels) was drawn over the VPL at the location of the injury on the structural images. Next FC analysis was performed using Pearson’s correlation between this ROI taken as the seed and the VALiDATe29 atlas defined 18 cortical-subcortical regions encompassing the whole brain. We also repeated the seed analysis with the S1 regions ROI separately. FC measures for each of the seed analyses were computed at each time point for the 3 monkeys and compared using statistical tests.

2.6. Behavioral assessment

For quantitative evaluation of hand use performance, each squirrel monkey underwent pre-injury training to perform a sugar pellet grasping task. This task involved retrieving a small sugar pellet from one of four wells on a modified Kluver board (numbered from well 1 to well 4) with varying depths and diameters, that determined task difficulties (Qi et al., 2013). Success rate was calculated as the ratio of pellets successfully retrieved to total trials. A secondary measure involved determining the number of flexes required per successful trial. A successful trial was defined as grasping and transferring the sugar pellet into the mouth using the designated hand.

The behavioral recovery time was divided into 3 timeframes-within 15 weeks, 15–40 weeks and above 40 weeks. Based on the BNC measures of IC1 at the post-injury stages through the first 9 weeks, an algorithm was developed to predict the behavioral recovery timeline. The algorithm constituted an if-else decision rule to predict the behavioral recovery period of the monkeys. This is represented in the form of a binary tree multi-class classification model. Two binary trees were generated: Binary-Tree 1 and Binary-Tree 2 for the behavioral prediction. Binary-Tree 1 used the difference between pre- and post-lesion FC difference at Stage2 and Binary-Tree 2 used the difference between pre- and post-lesion FC difference at Stage3 to correctly predict the recovery period (<15 weeks, 15–40 weeks or >40 weeks) for the 3 monkeys in our study.

2.7. Electrophysiology data acquisition and analysis

Electrophysiological recordings were conducted on two of the thalamus injured monkeys at two time-points, 6–7 weeks post-injury, and at 46–47 weeks post-injury. Two 32-channel, linear-array microelectrodes, with 100 μm inter-contact spacing (Plexon Inc, V-probe), were inserted into the digit regions of area 3b and area 1 of S1 cortex. Extracellular neuronal signals passed through a head stage (CerePlex Direct System, BlackRock Neurotech) and then were low pass filtered at 250 Hz to obtain local field potential (LFP) signals, which were digitized at 1 kHz. We collected a minimum of 6 to 8 resting state runs each comprising 10 min recordings of resting-state LFP signals for subsequent functional connectivity measures.

We developed a Matlab script along with the FieldTrip data analysis toolkit for processing the LFP signals and computed coherence measures for connectivity analysis for data filtered between 1–150 Hz (Wilson et al., 2016, Wu et al., 2017). We further computed percentage coherence differences for each monkey with respect to the normal side.

2.8. Histology

Post mortem each brain was sectioned in the coronal plane at 50 µm thickness and sections were stained for cytochrome oxidase or CO (Wong-Riley, 1979) in order to reveal the architectonic subdivisions within the thalamus. A lack of CO staining, indicating loss of neural activity on the injured side of the brain, was used to identify the extent of the lesion, similar to a previous work (Jones et al., 1997). Whole slide images were digitized at 10x magnification on a microscope (Carl Zeiss microscope, 2011) for quantification in ImageJ software.

We measured: 1) Percentage of VPL damaged by comparing the VPL region on both sides of the thalamus; 2) Percentage of lesion within the ipsilateral thalamus; and 3) Percentage of thalamus shrinkage on the lesion side by comparing the thalamus areas on both sides. The results were compared across the three animals.

2.9. Statistical analysis

Statistical analyses of the changes over time in WNC of IC1 were performed using a one-way ANOVA for each individual monkey with the timepoints (weeks) being treated as different groups. Similar analyses were performed for LFP coherences at different time-points, BNC changes from the combined 15 networks, as well as individual networks (including IC1) and the FC changes with the lesion as the seed point. The details of these are provided separately as Supplementary Tables in the SI document. The details include the overall F value, P value and R squared value of the ANOVA tests as well as the sum of squares (SS), DF, F value and P value for between the groups (time-points) and between the ICs as Main effects and residual as random effects. For multiple comparisons correction we used the Tukey’s multiple comparison test with an alpha set at 0.05 for establishing a significant difference between the means, and the corrected P-value for each group versus group comparison (after correction) as well as the total number of comparisons is also provided.

Further details of the different sections of the methods are provided in SI.

3. Results

3.1. Thalamus lesion evidence in MRI structural imaging

Each monkey underwent MRI acquisition 1–2 weeks following unilateral lesion, and imaging indicated that all 3 monkeys sustained VPL injury. A T2* weighted MR image of a section through the brain of one representative monkey (M3) depicting the thalamus lesion process is shown in Fig. 1A. Dark spots in the image signifying injury effects in the VPL region of the thalamus are visible at 1 week after injury (Fig. 1A, bottom row). Fig. 1B shows a corresponding section from a squirrel monkey brain atlas (Gergen and MacLean, 1962) containing the VPL for visual comparison. We also grouped the MRI acquisitions for each monkey into three stages to compare between monkeys, and the timeline of different experimental procedures including MRI acquisitions, electrophysiology recordings, behavior tasks, and the end-point histology assessment is depicted in Fig. 1C.

Fig. 1.

Fig. 1

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Thalamic Lesion process in monkey brain and experimental pipeline for the 3 monkeys. Panel (A) upper row shows MRI of a representative normal monkey (M3) which is subjected to ibotenic acid and electric current at the thalamus VPL. The lower row shows the MRI from the same monkey after 1-week post injury. Both axial and coronal view on T2* weighted structural images are shown. The arrow points to the thalamus VPL which shows hypointense spots representative of VPL injury. Panel (B) shows a plate from a squirrel monkey brain atlas at stereotaxic anterior-posterior level (AP 4.5–4.6) similar to the coronal view of the MRI (left), which includes a portion of the ventroposterior lateral nucleus (VPL). Scale in 1 mm intervals from the midline. Modified from Gergen and MacLean, 1962. Panel (C) shows the experimental timeline for MRI, electrophysiology, behavior and histology from the 3 monkeys. It also provides information on the demographics viz. age, weight and gender of the monkeys.

3.2. Resting state fMRI and LFP based connectivity measures from S1 region

FC analyses within the somatosensory network (IC1) containing the primary somatosensory cortex (S1) regions reveal that all three monkeys experienced a drop in WNC measures between Stage1 and 2, with statistically significant decreases in Monkey M1 and Monkey M3, as shown in Fig. 2A. After Stage2, there were gradual recoveries for both M1 and Monkey M2, with M1 recovering back to baseline measures (p > 0.05) by the end of Stage3. However, WNC measures for M3 remained significantly below baseline (p < 0.0001) untill the end of Stage3. The three monkeys are referred to as M1, M2 and M3 in the rest of this report.

Fig. 2.

Fig. 2

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Connectivity measures (absolute values) using fMRI and LFP from Somatosensory region. (A) show the spatial location of IC1 representative of the somatosensory network (including S1 cortex) in three planes: sagittal, coronal and axial respectively. This IC is derived from our previous study on 14 normal monkeys and the network location is being used as the ROI for the current study. (B) Changes in WNC of the IC1 at different time points post injury from the 3 thalamus injured monkeys. Bar plots show the mean +/-SD of the WNC measures computed from different runs at each time point with percentage change compared to baseline shown for each stage. Significantly different connectivity changes based on Tukey’s multiple comparison test are shown using * where * is p < 0.05, ** is p≪0.01, *** is p < 0.001 and **** is p < 0.0001. (C) An illustration of microelectrode mapping and recording sites in areas 3b and area1 of S1 cortex in a representative monkey. The green square box on the brain surface represent the field of view of the blood vessel photomicrograph image shown below. The invivo blood vessel image shows the electrophysiology map for digit representation guide for the insertion of micro-electrode for neural signal recording. To the right are brain MRI scans showing a demonstration of electrode insertion from the thalamus lesioned brain. The schematic in the center illustrate the recording setup and connectivity among the thalamus, area 3b and area1. (D) Coherence measures computed from LFP at 2 different time points (6–7 weeks and 46–47 weeks) for two monkeys: Monkey 2 (M2) and Monkey 3 (M3). Bar plots (Mean +/- SD) shows the coherence values at each time with percentage change in coherence compared to the normal side from Week 46–47 denoted. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

LFP were analyzed by computing the coherence between groups of neurons located in area 3b and area 1 of the S1 cortex and compared with the rsfMRI findings. The LFP based electrophysiology results from Weeks 6–7 post-injury revealed that M2 had a significant (p < 0.0001) increase in functional coherence compared to baseline while there was a small dip (p > 0.05) in coherence for M3 as shown in Fig. 2B. By the end of Weeks 46–47, M2 demonstrated further increase in coherence compared to Week 6 whereas M3′s coherence decreased significantly (p < 0.0001). The overall LFP measure changes at the two time-points indicate that M3 experienced substantially more functional damage compared to M2.

Supplementary Fig. S1 shows the pattern of WNC changes of somatosensory network IC1 post injury of the 3 thalamus injured monkeys compared to the baseline metrics obtained from 14 normal monkeys. The same trend of decline in FC post injury was observed for all 3 monkeys followed by a recovery phase for two of them (M1 and M2). While M1 and M2 recovered back to baseline by Week 8/Week9, M3 stayed below the baseline.

3.3. Global fMRI connectivity measures from fifteen independent networks and IC1

In addition to the local within-network analysis, we also performed between-network analysis to assess the global effects across the whole brain as well as for IC1 for the 3 animals. The BNC measures combined from all 15 ICs exhibited a notable decline post-injury at Stage1 from their baseline values for all the monkeys as illustrated in violin plots of Fig. 3A. M1 demonstrated a marked escalation in connectivity at Stage2 (p < 0.0001) and further in Stage3. However, M2 and M3 had a significant (p < 0.001) diminishment in connectivity at Stage2, followed by a slight increase at Stage3 that still remained notably (p < 0.0001) below baseline values. Fig. 3B violin plots show the BNC measures with respect to the somatosensory network IC1 only. A similar trend is observed for all the three monkeys with a sharp decline in FC for all three monkeys at Stage1 (p < 0.0001) which stayed significantly below baseline (p < 0.0001) untill Stage3 for two of them (M2 and M3) while recovering back to baseline for M1. This pattern was similar to that of the combination of 15 networks (Fig. 3A) although the percentage of FC change was higher for IC1. Hence the FC measures from IC1 were used for the behavioral task performance prediction modelling later in the study. Also, the percentage of FC reduction was higher in M3 than M2 at both Stage2 and Stage3 for both IC1 and the combination of 15 networks indicating higher injury related FC change in M3 compared to M2 while M1 had the least change.

Fig. 3.

Fig. 3

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Differential patterns of FC change (absolute values) between the networks from 3 monkeys post thalamic lesion. (A) shows the violin plot values from the combined 15 networks and (B) shows the values from the Somatosensory network (IC1). Significantly different connectivity changes based on Tukey’s multiple comparison test are shown using * where * is p < 0.05, ** is p < 0.01, *** is p < 0.001 and **** is p < 0.0001. The stages for injury were classified as Stage1 (Week 1/2), Stage2 (Week 4/5) and Stage3 (Week 8/9). Percentage change compared to Pre-lesion time is shown for each stage.

Supplementary Fig. S2 shows the comparison of FC measures of IC1 of the 3 monkeys with that of the baseline values from 14 normal monkeys. The same pattern was observed for FC changes from the group baseline values and from the individual pre-injury measures for all the 3 monkeys. The percentage of FC changes from the group baseline was lesser than that compared to its individual pre-injury values, but they were none-the-less significant (p < 0.001).

The BNC measures of individual ICs from one monkey M1 is depicted in Fig. 4A. ICs were grouped into three classes based on their pattern of change. Most of the cortical networks including somatosensory networks (IC1, IC3, IC6), sensorimotor network (IC4), auditory network (IC5) and default-mode network (IC7) decreased in connectivity significantly post-injury at Stage1, followed by a subsequent return to baseline values by Stage3. These are shown in the first 2 rows of Fig. 4A. The other set of ICs showed no significant change initially but started to show significant increase in FC from Stage1 onwards (Fig. 4A, 3rd row). The visual-cortex network (IC9) was the only IC with a significant increase in connectivity immediately post-injury (Fig. 4A, 4th row). The significant changes (p < 0.05) of these ICs between the different stages are shown in Fig. 4B. These are arranged inorder of their pattern of change as represented in Fig. 4A. The locations of all the fifteen independent components and the networks they represent are provided in the table in Fig. 4C. Supplementary Fig. S3 shows the detailed connectivity changes for each of the 15 networks.

Fig. 4.

Fig. 4

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Differential patterns of change in BNC (absolute values) of the 15 ICs from one monkey (M1) post thalamus lesion. (A) Each panel of boxplots represents the change in BNC over the time-points from one of the ICs. The boxplot of ICs is arranged in rows representing differential patterns of change in BNC values. The first two rows represent ICs which showed significant decline post injury at Stage1, the 3rd row represent ICs which didn’t show significant change at Stage1, but increased significantly from Stage2 and the 4th row represent the single IC which started to increase significantly from Stage1. (B) Significant changes (p < 0.05) in connectivity based on Tukey’s multiple comparison test between Pre vs. Stage1, Stage1 vs. Stage2 and Stage2 vs. Stage3 is shown as bar-plots for each IC. (C) Table showing the location of each of the fifteen ICs and the large-scale brain networks they represent.

The seed-based connectivity analysis of the lesion with the VALiDATe29 atlas defined 18 cortical-subcortical regions as well as separately with the S1 region yielded similar observations. M1 showed the fastest recovery (p < 0.05) in FC measures starting at Stage1 using both the seed-based analysis. M2 and M3 didn’t shown significant recovery pattern after injury and stayed below the baseline through Stage3. However, between M2 and M3, M3 showed greater significant decline in FC measures than M2 (p < 0.01 vs p < 0.05) through Stage3, based on the seed analysis with S1 region. The results of the seed-based analysis are shown in the Supplementary Fig. S4.

3.4. Behavioral task performance measures and behavioral recovery prediction algorithm

The behavioral metrics from the sugar-pellet grasping task were used to quantify the hand-use functional deficits post the thalamus VPL injury. For M1 the number of digit flexes (or attempts) per successful trial (Fig. 5A top row) during the pre-lesion period was recorded as 1 for Well 1 and approximately 1.06 for Well 3. After injury, the number of digit flexes required to successfully retrieve the pellets increased, peaking at approximately 1.78 for Well 1 by Week 5 and around 2.03 for Well 3 by Week 3 before gradually diminishing. By the end of the 14th week, the number of flexes reverted to ∼1 for Well 1 and 1.10 for Well 4. The success rate, which was the other behavioral measure of the proportion of successful trials, decreased post-injury for M1, reaching its nadir of 0.84 for Well 1 and 0.68 for Well 3 at Week 3 before exhibiting a non-monotonic recovery to their baseline values by the 14th week.

Fig. 5.

Fig. 5

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Behavioral task performance measures from two representative wells (Well 1 and Well 3) of two monkeys before and after injury. (A) shows the number of flexes and success rate obtained from behavioral task for the M1 (top row) and M2 (bottom row) over the course of their recovery. Monkey M3 didn’t have any behavioral recovery post injury and hence not shown. (B) shows the algorithm for behavioral recovery prediction for Well 1 in the form of a binary tree based on connectivity measures (BNC) of IC1 from Stage2 (top row) and Stage3 (bottom row).

Behavioral metrics for M2 (Fig. 5A bottom row) demonstrated that the number of flexes for Well 1 increased immediately after injury to ∼1.3 until the 12th week before it commenced recovery, reaching close to baseline values of 1.04 by Week 30, while the success rate for Well 1 began increasing after the 15th week, before stabilizing at approximately 0.82 by Week 37. However, M2 exhibited no improvement in performance on Well 3 until the last week tested (43rd week) for both the measures. The third monkey in our study, M3, was unable to perform any of the behavioral tasks on any well as observed until the 40th week. Thus, the results clearly indicated a gradation of effects, with M1 recovering the fastest in the behavioral task while M2 took longer time to recover and M3 didn’t recover pre-injury performance at all. Supplementary Fig. S5 shows the results from all the 4 wells for the two monkeys M1 and M2.

The results of the binary-tree based behavioral prediction model is shown in Fig. 5B. Binary-Tree 1 shows the prediction using Stage2 FC values, and Binary-Tree 2 shows the prediction using Stage3 FC values. Using appropriate values of pre- and post-lesion FC difference from any monkey’s IC1 region, the model can correctly predict their behavioral recovery period (<15 weeks, 15–40 weeks or > 40 weeks). Thus, if the difference between pre-lesion and Stage2 FC values is less than 0.18, recovery happens before 15 weeks. However, if the difference is between 0.18 and 0.43, the recovery takes 15–40 weeks, otherwise it takes greater than 40 weeks. Similarly, if the difference between pre-lesion and Stage3 FC values is less than 0.22, recovery happens before 15 weeks. However, if the difference is between 0.22 and 0.47, the recovery takes 15–40 weeks, otherwise it takes greater than 40 weeks.

3.5. Histological confirmation of the injury

Our histological analysis concentrated on the thalamic tissue sections encompassing the VPL. Fig. 6A shows a representative slice of the injured thalamus tissue containing VPL acquired post-mortem from a similar anterio-posterior level of the brain of the 3 monkeys. Quantitative analysis of the percentages of thalamus and VPL tissue damaged from the 3 monkeys are tabulated in Fig. 6B. The results showed that the VPL was damaged almost fully in M3 (99. 41 %) compared to incomplete damage to M2 (93.45 %) and even lesser damage to M1 (84.11 %). Between M2 and M3, both had similar percentages of total lesion areas within thalamus, although M2 had higher thalamus shrinkage. While injury effects were present in all sections of M2, either fully or partially, no VPL damage was observed in M1 in the most anterior sections. It is to be noted that we performed a second procedure on M1 after its behavioral recovery, so the endpoint thalamus damage is slightly overestimated. Nevertheless, the histology findings conclusively revealed that M3 experienced the most severe VPL tissue damage, whereas M1 incurred the least and M2 in between them. These histological results align with the behavioral metrics of the 3 monkeys, which in turn can be predicted by FC measures with the binary-tree based prediction model. Further details on histology results are provided in SI.

Fig. 6.

Fig. 6

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Histology results from the three thalamus lesioned monkeys. (A) Representative histological slices obtained from high resolution scanning of the cytochrome oxidase-stained brain sections of the three monkeys. The normal thalamus and its VPL are outlined in white dotted lines, the ipsilateral thalamus portion that is intact after the injury is outlined in orange and the lesioned region is outlined in red. The estimated VPL on the ipsilateral side is outlined in white. Each small square measures 1.25 mm. (B) Table shows the percentage area of different regions calculated from the thalamus after injury to the three monkeys. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

4. Discussion

Measures of functional connectivity based on resting state fMRI are readily computed, and changes in FC may be used to infer the effects of an intervention or differences between groups of subjects, but there have been few studies performed that validate the interpretation of FC or prove their direct correspondence to specific functions or behaviors. Natural causes of thalamic injury to the brain encompass a spectrum of etiologies, including traumatic and mild-traumatic brain injuries (TBI and MTBI) stemming from various sources such as motor vehicle accidents, sports-related injuries, and falls. Additionally, ischemic stroke, hemorrhage (including intracerebral and subarachnoid hemorrhages), and tumors such as thalamic gliomas represent significant contributors to thalamic damage. This study explores the potential utility of rsfMRI metrics as a dependable non-invasive imaging biomarker for monitoring the deficits resulting from thalamic brain injury in squirrel monkeys and compare its alterations with other measurements such as electrophysiology, histology and behavioral task performance in a longitudinal study.

4.1. Agreement of resting state fMRI with electrophysiology, behavior and histology results

Sensory information flows from thalamus VPL to primary somatosensory cortex (S1), which receives direct and indirect neuronal inputs from the thalamus (Cusick and Gould, 1990, Haueisen et al., 2007, Tauste Campo et al., 2024). RsfMRI based FC analysis within the somatosensory network (IC1) indicated that monkey M3 experienced greater local connectivity damage compared to M1 and M2 after the thalamus VPL lesion. We collected data from two of the three monkeys to compare the FC measures with the LFP based coherence measures obtained from neurons located in area 3b and area 1 of the S1 cortex. The overall coherence changes over the two time-points that LFP were acquired, indicate that M3 experienced substantially more functional damage compared to M2. Thus, the findings from these two modalities suggest that rsfMRI local FC measures are consistent with electrophysiological data within the somatosensory local network. We also computed the between network FC analysis from both the somatosensory network (IC1) as well as the 15 networks combined, which revealed the greatest damage in M3 followed by M2 while M1 experienced the least damage and was the earliest to recover. This was compared with the behavioral metrics from the sugar-pellet grasping task as it is a sensitive way of quantifying hand use recovery after an injury to the hand representation within the somatosensory system (Barbay et al., 2021, Frost et al., 2003, Nudo et al., 1992) or spinal cord (Duque et al., 2022, Qi et al., 2013) of monkeys. Each of the 3 monkeys in our study had significantly different behavioral recovery timelines, with M1 recovering the fastest and M3 showing no recovery until the 40th week. Thus, the behavioral recovery timeframes of all 3 monkeys followed the same gradation as that of the functional connectivity measures obtained from the between-network connectivity measures. Moreover, the rsfMRI changes preceded the behavioral recovery pattern which motivated us to build a prediction model using the functional connectivity measures of IC1. The developed binary-tree model could predict the recovery timeline based on these measures as early as Stage2 (Week 4–5) post injury. Individual demographic variability can be a source of variability in the behavioral recovery timeline, but in our study group the monkeys were all the same sex (male), and similar age and weight range (see above) rendering their influence to be minimal on the recovery process. Finally, we also compared our rsfMRI results with the histological data which is widely regarded as the gold standard for quantifying tissue damage following injury. The histological data also revealed that M3 experienced the most severe VPL tissue damage, whereas M1 incurred the least and M2 in between them. Thus, the resting-state fMRI metrics also align with the histological findings from these 3 monkeys. Our findings from the seed-based FC analysis with the seed located at the lesion region also supported the results from IC based FC analysis i.e. M1 started to recover earliest based on FC measures while between M2 and M3, the latter suffered higher significant decline in FC values, with both remaining below the baseline until Stage3. Overall, the inter-subject differences in rsfMRI based FC measures agreed very well with electrophysiology and histopathology as well as the behavioral performance measures.

4.2. Global effect of thalamus VPL injury on brain functional networks showing differential patterns of recovery

Previous fMRI research on thalamic injury has predominantly concentrated on intra-thalamic connections or thalamo-cortical connections with thalamus nuclei as the seed region (Tang et al., 2011, Woodrow et al., 2023). Significantly increased thalamocortical functional connectivity was observed in MTBI patients in one study (Tang et al., 2011). In a more recent study, increased functional connectivity within 23 pairs of thalamic nuclei were also observed post MTBI (Woodrow et al., 2023). However, other studies have suggested that the thalamocortical system exhibits a complex information transfer pathway, resulting in indirect connections between cortical regions while integrating information across global multifunctional pathways (Saalmann, 2014, Sherman and Guillery, 2013). The identification of fifteen major functional networks, encompassing various cortical and sub-cortical regions in the squirrel monkey brain, provided us a robust platform to investigate this organization using resting-state fMRI (Sengupta et al., 2023).

As anticipated, somatosensory networks such as IC1, IC3 and IC6 showed a marked decline in BNC at Stage1 indicating injury-related damage. These connectivity changes have a cascading effect, spreading to other brain regions. For instance, the auditory network in IC5 also exhibited an initial decline in connectivity similar to the somatosensory networks. The vmPFC (IC7 of executive control network) which is responsible for decision making and has been implicated in a variety of cognitive functions also showed significant reductions post-injury (Hiser and Koenigs, 2018, Szczepanski and Knight, 2014). The cerebellum, which is a part of the IC2 (sub-cortical network) and is primarily responsible for the coordination of movement, maintaining posture and balance, muscle tone, and motor learning, also had a sharp drop in connectivity with other networks post injury (Roostaei et al., 2014, Schmahmann, 2019). All these networks showed a gradual recovery towards baseline values with time. On the other hand, the visual network displayed a different pattern of connectivity change. The visual network connections (IC9 and IC15) had very low values pre-injury (r ∼ 0.2), but they showed a notable increase in connectivity immediately post-injury starting at Stage1 (IC9) or Stage2 (IC15) and by the end of Stage3, their values rose to r∼0.7. This increase in visual network connectivity post-injury could be a compensatory mechanism of the brain in response to the motor and sensory deficits. We also saw a marked increase in thalamic network’s functional connectivity post-injury at Stage2, as visible from the significant increase in correlation values of IC10 (r ∼ 0.4 to ∼0.7) for M1 echoing the findings of thalamo-cortical connectivity changes in MTBI patients (Banks et al., 2016, Tang et al., 2011, Woodrow et al., 2023). It can be added here that the behavioral task performance may be influenced by the functional integration between various brain regions viz. hand-eye coordination involving the visual network (IC9, IC15), which is necessary to visually locate the pellet location and grasp them accurately, or the posterior parietal cortex of the sensorimotor network (IC4) which incorporates sensory inputs to inform and shape motor outputs (Bai et al., 2021, Malhotra et al., 2009), apart from the somatosensory network (IC1, IC3, IC6) which detects sensory information from the body.

Furthermore, the differential trends of network connectivity changes are evident in their temporal patterns throughout the recovery process. While some network’s FC values experienced a significant change from pre-injury to Week 4, others showed significant changes starting a few weeks post-injury (Week 2 to Week 4), while some other ICs underwent notable changes only later during the recovery i.e. Week 4 to Week 8. Overall, our study reveals a global impact following thalamic brain injury with differential patterns of FC change across various networks over the course of observation.

4.3. Utility of normal monkey dataset for assessing FC changes post thalamic lesion

The baseline rsfMRI data from 14 normal monkeys provided similar trends in changes in WNC of somatosensory network (IC1) as that of the individual monkey’s prelesion values, although the percentage change was lesser and not statistically significant. The same conclusion was observed from BNC measures of IC1 of 14 normal monkeys with post-lesion changes being statistically significant too. Overall, using baseline rsfMRI data from normal monkeys is beneficial in assessing the recovery process post-injury, especially in cases where pre-injury values are unavailable, as is often the case in clinical scenarios.

4.4. Limitations

One drawback turned advantage of the current study was that although thalamus VPL was targeted using receptive field mapping, there were differences in the extents of VPL injury in each of the monkey as confirmed through histology. The different extent of injury affected the recovery process differently in the three animals, to which FC measurements could be compared. Another limitation of this study was the small sample size although the data from the three monkeys yielded sufficiently distinctive behavioral and other brain assessment measures to make the necessary conclusions. Also, the animals of our study were anesthetized during image acquisition and it has been reported that anesthesia could influence the measurement of functional connectivity (Hutchison et al., 2014). However, a high correspondence was observed in previous studies between connectivity measures of rsfMRI signals and local field potentials obtained under anesthesia conditions (Wu et al., 2019b).

5. Conclusion

The results of this study suggest that rsfMRI measurements of FC reflect a behaviorally relevant index of functional integrity and can serve as a predictive biomarker for assessing recovery time following a thalamic brain injury. Furthermore, the inter-subject differences in rsfMRI based FC measures following the injury corresponded well with other invasive modes of assessment like electrophysiology and histopathology. This study also underscores the fact that an injury to the thalamic VPL exerts a cascading effect to different brain regions, which highlights the necessity of whole-brain studies to gain comprehensive insights into the injury process.

CRediT authorship contribution statement

Anirban Sengupta: Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Conceptualization. Pai-Feng Yang: Validation, Methodology, Formal analysis, Data curation. Jamie L. Reed: Writing – review & editing, Validation, Methodology, Formal analysis, Data curation. Arabinda Mishra: Visualization, Methodology, Formal analysis. Feng Wang: Methodology, Data curation. Isaac V Manzanera Esteve: Software, Formal analysis. Zhangyan Yang: Methodology, Data curation. Li Min Chen: Writing – review & editing, Validation, Supervision, Resources, Project administration, Funding acquisition, Conceptualization. John C. Gore: Writing – review & editing, Validation, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

The study is supported by National Institute of Neurological Disorders and Stroke (NINDS) under National Institutes of Health (NIH) grant R01 NS078680 (Project Investigator: JCG and LMC).

The authors acknowledge Chaohui Tang, Yue Zou and Fuxue Xin for animal handling and scan assistance and Daniela Hernandez Duque for handling behavioral data.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.nicl.2025.103753.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Supplementary Data 1
mmc1.docx (1.6MB, docx)

Data availability

Data will be made available on request.

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Associated Data

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Supplementary Materials

Supplementary Data 1
mmc1.docx (1.6MB, docx)

Data Availability Statement

Data will be made available on request.

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