Interhemispheric parietal-frontal connectivity predicts the ability to acquire a nondominant hand skill

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Interhemispheric parietal-frontal connectivity predicts the ability Interhemispheric parietal-frontal connectivity predicts the ability

to acquire a nondominant hand skill to acquire a nondominant hand skill

Benjamin A. Philip

Washington University School of Medicine in St. Louis

Mark P. McAvoy

Washington University School of Medicine in St. Louis

Scott H. Frey

University of Missouri

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Recommended Citation Recommended Citation

Philip, Benjamin A.; McAvoy, Mark P.; and Frey, Scott H., "Interhemispheric parietal-frontal connectivity

predicts the ability to acquire a nondominant hand skill." Brain Connectivity. 11, 4. 308 - 318. (2021).

https://digitalcommons.wustl.edu/open_access_pubs/10404

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Interhemispheric Parietal-Frontal Connectivity Predicts

the Ability to Acquire a Nondominant Hand Skill

Benjamin A. Philip,

1,2

Mark P. McAvoy,

and Scott H. Frey

Abstract

Introduction: After chronic impairment of the right dominant hand, some individuals are able to compensate

with increased performance with the intact left nondominant hand. This process may depend on the nondominant

(right) hemisphere’s ability to access dominant (left) hemisphere mechanisms. To predict or modulate patients’

ability to compensate with the left hand, we must understand the neural mechanisms and connections that under-

pin this process.

Methods: We studied 17 right-handed healthy adults who underwent resting-state functional connectivity (FC)

magnetic resonance imaging scans before 10 days of training on a left-hand precision drawing task. We sought to

identify right-hemisphere areas where FC from left-hemisphere seeds (primary motor cortex, intraparietal sulcus

[IPS], inferior parietal lobule) would predict left-hand skill learning or magnitude.

Results: Left-hand skill learning was predicted by convergent FC from left primary motor cortex and left IPS

onto the same small region (0.31 cm

) in the right superior parietal lobule (SPL).

Discussion: For patients who must compensate with the left hand, the right SPL may play a key role in integrat-

ing left-hemisphere mechanisms that typically control the right hand. Our study provides the ﬁrst model of how

interhemispheric functional connections in the human brain may support compensation after chronic injury to the

right hand.

Key words: fMRI; functional connectivity; laterality of motor control; learning; movement

Impact Statement

This article presents the ﬁrst model of how the human brain applies left-hemisphere (dominant hand [DH]) specializations to

support the right hemisphere for compensatory action with the nondominant hand (NDH). Compensation with the NDH is

critical for the rehabilitation after many neurological disorders that lead to irreversible impairment of the DH (e.g., stroke,

amputation, nerve injury), but no effective therapies exist to promote compensation because the neural mechanisms remain

unknown. Therefore, this article will open up new directions in rehabilitation neuroscience by presenting a testable model of

the connections that underpin much needed (and currently nonexistent) therapies.

Introduction

ndividuals who suffer from chronic unilateral impair-

ment of their dominant hand (DH) must use their intact

nondominant hand (NDH) to maintain self-sufﬁciency (‘‘com-

pensation’’ [Jones, 2017]), but it remains unknown how con-

nectivity in the human brain supports this process. Chronic

unilateral impairment of the DH can occur after a variety of

clinical conditions, including peripheral nerve injury and am-

putation. The United States has 20 million peripheral nerve

injury patients (Lundborg, 2003), and *166,000 surgical

treatments occur every year to treat peripheral nerve injury

of the right upper extremity (Brattain, 2013; Kouyoumdjian

et al., 2017; Philip et al., 2017; Taylor et al., 2008). Thirty-

three percent to 39% of upper extremity nerve injury patients

never achieve satisfactory motor recovery after surgical repair,

regardless of any rehabilitation they receive (Council, 1943;

Dyck et al., 2005; He et al., 2014). This leaves 55–66,000 pe-

ripheral nerve injury patients per year who are not adequately

served by rehabilitation strategies that focus on restoring lost

Program in Occupational Therapy, Washington University School of Medicine, St. Louis, Missouri, USA.

Department of Psychological Sciences, University of Missouri, Columbia, Missouri, USA.

Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA.

BRAIN CONNECTIVITY

Volume 11, Number 4, 2021

ª Mary Ann Liebert, Inc.

DOI: 10.1089/brain.2020.0916

308

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function (Langhorne et al., 2009). To help these patients, we

must understand the neural mechanisms and connections

that enable compensation after DH injury.

Compensation is difﬁcult, but possible. In right-handed

humans, the DH is specialized for intersegmental dynamics

and precision movement (Sainburg and Kalakanis, 2000;

Sainburg, 2002), so a key challenge is achieving NDH per-

formance at precision movements (e.g., writing, drawing).

Amputees who lose their DH eventually achieve the ability

to perform skillful precision movements with the intact

NDH (Philip and Frey, 2014), but this may require decades

of exclusive forced use of the NDH. Healthy adults can im-

prove NDH precision performance with as little as 10 days of

training (Philip and Frey, 2016), but other studies have

shown marked interindividual differences in the ability to ac-

quire NDH skill. NDH handwriting studies in healthy adults

(Walker and Henneberg, 2007) and amputees (Yancosek and

Mullineaux, 2011) show great interindividual variability,

which suggests that some individuals may be better able to

achieve NDH performance than others.

The neural mechanisms of NDH compensation are likely to

involve interhemispheric communication in the cerebral cor-

tex. The DH—and thus the left hemisphere—contains special-

izations that support precision movement with both hands

(Mani et al., 2013; Sainburg and Kalakanis, 2000; Sainburg,

2002; Schaefer et al., 2009) (For clarity, we refer to right-

handed individuals only, so the DH is supported by the contra-

lateral left hemisphere.). For left-hemisphere specializations

to support ipsilateral NDH movement, those specializations

must involve the minority (10–25%) of uncrossed descending

pyramidal ﬁbers (Davidoff, 1990), or interhemispheric con-

nections to right-hemisphere motor areas with direct control

of the NDH. Interhemispheric connections are likely to play

the primary role in precision NDH movement: the timing of

interhemispheric connections indicates a functional rather

than epiphenomenal role (Fiori et al., 2017), and interhemi-

spheric coordination depends on task demand (Wischnewski

et al., 2016). Interhemispheric communication for motor con-

trol is primarily transcallosal (Doron and Gazzaniga, 2008),

especially in the form of interhemispheric inhibition (Ferbert

et al., 1992; Hortoba

gyi et al., 2011; Kobayashi et al., 2003;

Pelled et al., 2009) that arises from transcallosal connections

between left and right primary motor cortex (M1). However,

transcallosal connectivity is not dominated by motor cortices:

callosotomy patients show greater interhemispheric discon-

nection between parietal regions than between primary senso-

rimotor regions (Roland et al., 2017).

Our goal was to identify the interhemispheric neural mech-

anisms in the human brain that support people’s ability to im-

prove NDH performance at a precision drawing task (PDT).

To achieve this, we re-evaluated data from an existing

study of NDH precision movement training in healthy adults

(Philip and Frey, 2016), to identify pretraining functional

connectivity (FC) patterns that predicted individuals’ later

ability to improve their NDH performance. Participants re-

ceived resting-state functional magnetic resonance imaging

(MRI) scans before 10 days of NDH training (*18 min/

day). We hypothesized that interhemispheric communication

involving left-hemisphere primary motor cortex hand area

(M1-Hand, or M1 for brevity), intraparietal sulcus (IPS), or

inferior parietal lobule (IPL) would predict ﬁnal NDH perfor-

mance or magnitude of NDH learning. We found that inter-

hemispheric connectivity between left M1 and IPS onto the

same small region in right superior parietal lobule (SPL) pre-

dicted subsequent ability of healthy adults to improve their

NDH performance.

Materials and Methods

Experimental design overview

Full methods have been previously published in Philip and

Frey (2016). In brief, participants used their left NDH to prac-

tice the PDT for 10 days or until their NDH performance

reached an a priori criterion of ‡80% of their baseline DH

speed on two consecutive training days. DH baseline perfor-

mance was recorded before and after NDH training. Partici-

pants underwent MRI scanning before the ﬁrst PDT session

and 1 day after the ﬁnal PDT session. This study investigated

relationships between pretraining MRI and two outcome vari-

ables: peak PDT performance and magnitude of PDT learning.

Data collection was performed with the approval of the

University of Missouri Institutional Review Board and com-

pleted in accordance with the Declaration of Helsinki. These

data are not associated with a Clinical Trials Registration

Number because they were collected in 2012–2013.

Participants

We excluded individuals who were musicians, knitters, or

had other hobbies involving precision bimanual skills.

Twenty-two participants completed the behavioral study

(age 29 – 11, 15 female). All were right handed (Edinburgh

scores 90 – 16; Oldﬁeld [1971]). Three participants were ex-

cluded from detailed analyses due to noncompliance (see

Philip and Frey, 2016, for details). One participant did not

complete the MRI study, and another participant’s MRI

data were discarded due to excess motion (for details, see

MRI Data Analysis below), leaving 17 participants for this

study (age 27 – 8, 12 female).

Precision drawing task

Participants used a pen stylus to draw a line through each

stimulus while remaining within the boundaries of mirror-

symmetrical geometric forms (Fig. 1). The PDT was pre-

sented on a Cintiq 12wx tablet with integrated 1200*800

screen (Wacom Co., Otone, Japan), controlled by Presenta-

tion software, v.16.0 (Neurobehavioral Systems, Inc.,

Albany, CA). This system allowed participants to draw di-

rectly on the tablet screen, during which endpoint speed

and position were recorded at 30 Hz.

Each stimulus was built from 2, 3, or 4 geometric elements

(lines or semicircles); each element had a drawing length of

45 mm, and thus total path lengths of 90, 180, or 360 mm. Pre-

cision requirements were manipulated by varying stimulus

width (3, 4, or 5 mm). The full set of possible PDT stimuli

entailed 15 shapes (5 of each length), each presented at all 3

widths, producing a total of 45. NDH training sessions entailed

90 trials, 2 repetitions of all 45 stimuli. DH sessions entailed 2

repetitions of 12 shapes (4 of each length) at 2 widths (3 and

5 mm), producing a total of 48 stimuli. Trials were presented

in a unique pseudorandom order during each session.

Participants were instructed to move as quickly as possible

without making any errors; that is, they were instructed to

prioritize error minimization over speed. A trial was deﬁned

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as an ‘‘error’’ if the drawn lines ever passed outside the stim-

ulus margin; except for distinct accidental lines created as

part of raising or lowering the pen, and for overshoots at

the end of segments (following Beery and Beery, 2004). A

trial was considered ‘‘incomplete’’ if the drawn lines did

not pass fully through all open paths within the form. A

trial was considered a ‘‘success’’ if it was neither an error

nor incomplete. Both successful and error trials were used

for further analyses, to keep trial counts constant across par-

ticipants and training sessions.

To initially familiarize subjects with the task require-

ments, a DH practice session preceded the DH1 session,

and a NDH practice session preceded the NH1 session.

Each of these familiarization sessions consisted of 15 trials

(15 shapes, 1 repetition, all at 4 mm width).

Because the number of trials per session was ﬁxed, session

duration decreased over time as participants became more

skilled and increased their movement speed. NDH training

session #1 took 21 – 3 min, while the ﬁnal session took

17 – 3 min. Across all training sessions and participants,

total training time averaged 182 – 30 min.

Behavioral analysis

The tablet collected x and y positions of the pen endpoint

at 30 Hz. During analysis, velocity proﬁles for each trial were

smoothed using an acausal Gaussian ﬁlter with sigma of

three samples (90 ms).

Ratings of trial status (success, error, or incomplete) were

performed by hand. A senior experimenter’s ratings were

validated by a second experimenter. Raters were not blinded,

because data were rated on the day of collection. For any ses-

sion in which within-session agreement was <0.93 or inter-

rater reliability (IRR) was <0.7 (mean 0.5 standard deviation

[STD] from Philip and Frey [2014]), the two experimenters

rerated the session collaboratively until they reached consen-

sus on every trial. Final ratings reached 0.98 – 0.01 agreement

and 0.91 – 0.04 IRR across participants.

This study used endpoint movement smoothness

(‘‘smoothness’’ for brevity) as its outcome measure, based

on previous ﬁndings that smoothness provided the most

hand-speciﬁc performance gains during this task (Philip

and Frey, 2016), and because movement smoothness best

captures the DH-speciﬁc (left-hemisphere) specializations

for feedforward control and precision smooth movement

(Sainburg and Kalakanis, 2000; Sainburg, 2002).

Smoothness was calculated as 1 * the number of velocity

peaks per 45 mm shape element, average for each trial.

Higher smoothness (closer to zero) represents fewer sub-

movements per movement. To measure performance

changes consistently across participants, smoothness was

normalized to each participant’s ﬁrst NDH session by con-

verting it into a ‘‘Z

NDH

’’ score. A Z

NDH

score was calculated

for each participant and session by taking participant mean

smoothness during that session, subtracting the participants’

mean during their ﬁrst NDH session, and dividing by the

STD during their ﬁrst NDH session. In other words, the

score reﬂects a Z-score based on the participant’s un-

trained NDH performance; this is similar to Cohen’s d mea-

sure of effect size (Cohen, 2013), but based only on the STD

during the ﬁrst NDH session.

MRI data collection

Scans were performed on a Siemens (Erlangen, Germany)

3T Trio using a standard birdcage radiofrequency coil. Dur-

ing each fMRI session, participants lay still with their eyes

open. Scan sessions included the following: (1) T1- and

T2-weighted structural scans, (2) three 5:42 min functional

runs using echoplanar imaging sensitive to the blood oxygen-

level dependent contrast (BOLD-EPI), and (3) a gradient

echo ﬁeld map.

BOLD scans used a T2*-weighted sequence with the fol-

lowing parameters: temporal resolution (TR) = 2240 ms,

echo time (TE) = 30 ms, 64 · 64 voxel matrix, ﬁeld of view

(FoV) = 256 mm, 36 contiguous axial slices acquired in inter-

leaved order, thickness = 4.0 mm, in-plane resolution =

4.0 mm, bandwidth = 2004 Hz/pixel, 150 volumes. The ini-

tial two volumes in each scan were discarded to allow

steady-state magnetization to be approached. Throughout

FIG. 1. Precision drawing

task. Five sample stimuli

shown, out of 45 possible.

310 PHILIP ET AL.

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the functional scans, participants were instructed to remain

awake with their eyes open and maintain ﬁxation on a cen-

trally presented point. Compliance was manually monitored

using an Eye-Trac 6000 (Applied Science Laboratories, Bed-

ford, MA) eye-tracking camera.

High-resolution T1-weighted structural images were also ac-

quired, using the 3D MP-RAGE pulse sequence: TR = 2500 ms,

TE = 4.38 ms, inversion time = 1100 ms, ﬂip angle = 8.0,

256 · 256 voxel matrix, FoV = 256 mm, 176 contiguous axial

slices, thickness = 1.0 mm, in-plane resolution: 1.0 · 1.0 mm.

DICOM image ﬁles were converted to NIFTI format using

MRI-Convert software.

MRI preprocessing

Image preprocessing was performed with 4dfp Tools.

Image preprocessing included the following steps: (1) com-

pensation for slice-dependent time shifts, (2) elimination of

odd/even slice intensity differences due to interleaved acqui-

sition, and (3) realignment of all data acquired in each sub-

ject within and across runs to compensate for rigid body

motion (Ojemann et al., 1997). The functional data were

transformed into Talairach atlas space (Talairach and Tour-

noux, 1988) by computing a sequence of afﬁne transforma-

tions (ﬁrst frame of BOLD run to T2-weighted image to

MP-RAGE to atlas representative target without compensa-

tion for local distortions between echo planar images and

anatomy), which were combined by matrix multiplication,

resampling to a 2 mm isotropic grid. For crossmodal (i.e.,

functional to structural) image registration, a locally devel-

oped algorithm was used (Rowland et al., 2005).

Resting-state FC models

Image analysis was performed with FIDL (Functional

Independent Data Language; ftp://imaging.wustl.edu/pub/

mcavoy, login as guest). The BOLD time series was mod-

eled with a general linear model that included lateralized

terms for the global signal (global), white matter (WM), and

ventricles (CSF; McAvoy et al., 2016). The global signal

regressor was formed from a whole brain mask that included

the cerebellum, WM, and CSF but not extra-axial CSF, while

WM and ventricular regressors were formed from each in-

dividual’s eroded WM and ventricular masks (Power et al.,

2014). Regressors were computed by removing the linear

trend and constant at each voxel, then averaging over voxels

within the respective hemisphere of the mask. To investigate

FC, the time series of the seed region was also included as a

regressor in the model. The seed regressor was formed by

removing the linear trend and constant at each voxel, then

averaging over voxels within the region.

Subject-speciﬁc general linear models (Friston et al.,

1995) were ﬁt to BOLD time series at each voxel as shown

schematically below:

BOLD = seed þ global L, RðÞþWM L, RðÞþCSF L, RðÞ

þ motion þ lpf þ c

þ c

(1)

where the notation L, RðÞrepresents 2 terms. Head move-

ment was accounted for by motion, 24 time series formed

from measured head shifts and angular displacements in

three dimensions (i.e., X, Y, Z, pitch, yaw, and roll) along

with their squares, derivatives, and squared derivatives (Fris-

ton et al., 1996). The low-pass ﬁlter lpf as implemented with

a Fourier basis set that consisted of sine and cosine pairs

modeling full cycles from the cutoff of 0.08 Hz to the

Nyquist frequency (Biswal et al., 1995; Lowe et al., 1998),

thus low-pass ﬁltering was accomplished with the inclusion

of high-frequency terms. Additional terms included the con-

stant c

and linear trend c

t to model slow drifts. The partial

correlation model of Eq. 1 was solved through ordinary least

squares, yielding estimated weights for all regressors.

Seed selection

For the purposes of analysis, 5 mm-radius spherical seed

regions were deﬁned in left M1, left IPS, and left IPL. The

M1 seed was based on normative sensorimotor hand area

(Philip and Frey, 2014). The parietal seeds were based on

previous analyses of the current dataset (Philip and Frey,

2016), and restricted to volume deﬁned as ‡25% chance of

being in the appropriate anatomical area according to the

Juelich Histological Atlas (Eickhoff et al., 2006, 2007).

Within left IPS, the voxel with peak activation was chosen

from areas that previously showed interhemispheric changes

related to learning—speciﬁcally, where training-related in-

creases in FC with right M1 correlated with NDH skill learn-

ing (see ﬁg. 4a in Philip and Frey, 2016). Within left IPL, the

voxel with peak activation was chosen from areas that previ-

ously showed interhesmipheric changes related to learning

and retention—speciﬁcally, where training-related changes

in FC with right M1 predicted long-term retention of NDH

skill (see ﬁg. 5a in Philip and Frey, 2016). These selected

voxels’ Montreal Neurological Institute coordinates were

as follows: left M1 X = 38, Y = 24, Z = 54; left IPS

X =  51, Y = 41, Z = 43; left IPL X = 55, Y = 22,

Z = 26. In all three cases, the seed was created as a 5 mm-

radius sphere centered on the peak voxel, and limited to

the volume with ‡25% chance of being in the appropriate an-

atomical area.

On a post hoc basis (after the other analyses described

here), a fourth seed region was deﬁned in left dorsal premo-

tor cortex (PMd) to determine whether PMd–PMd interhemi-

spheric connectivity contributed to learning of NDH drawing

performance. The PMd seed’s central voxel was selected as

the peak voxel of drawing-speciﬁc activity in left PMd in

previous studies (Potgieser et al., 2015) at X = 24, Y = 8,

Z = 50. The seed was created as a 5 mm-radius sphere cen-

tered on that central voxel, and limited to the volume with

a ‡ 10% chance of being in the appropriate anatomical

area. This cutoff was lower than the other seeds, because

the PMd peak voxel had only a 21% chance of being in Brod-

mann’s Area 6 according to the Juelich Histological Atlas

(Eickhoff et al., 2006, 2007), which made a 25% cutoff

unfeasible.

Correlation of behavior with resting-state FC

Separate FC models were computed for seeds in left M1,

left IPS, and left IPL (see Eq. 1). For each participant, the es-

timated voxelwise weights of the seed region were normal-

ized to the constant c

, then smoothed with a 4 mm full

width at half-maximum three-dimensional Gaussian kernel

to blur individual differences in brain anatomy. The Pearson

correlation was then computed between each participant’s

behavior (see next paragraph) and their measure of FC for

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each seed, respectively. The correlation maps were algebra-

ically converted to T statistics, then ﬁt to a normal distribu-

tion yielding gaussianized T statistics, which were corrected

for multiple comparisons (jzj ‡ 3.0, minimum 21 face-

connected voxels, p < 0.05 corrected) with a Monte Carlo-

based method (Forman et al., 1995; McAvoy et al., 2001).

For this analysis, four measures of behavior were used:

‘‘learning,’’ deﬁned for each participant as the increase in

movement smoothness between their initial session and the

session with peak smoothness (i.e., maximum Z

); ‘‘base-

line,’’ deﬁned as pretraining NDH smoothness; ‘‘forget-

ting,’’ deﬁned as the peak NDH smoothness minus NDH

smoothness at 6 months post-training; and ‘‘retention,’’ de-

ﬁned as NDH smoothness 6 months post-training. Behavioral

results have been previously published in detail (Philip and

Frey, 2016), but key values used for the current analysis

are shown in Table 1.

Results

Convergent connectivity onto right SPL predicts

NDH learning

NDH smoothness learning was correlated with interhemi-

spheric FC from left M1 seed onto a scattered selection of

right-hemisphere areas, shown in Figure 2, including SPL,

inferior frontal gyrus extending into insula, and multiple

areas in the basal ganglia. Smoothness learning was also

correlated with interhemispheric FC between left IPS and a

selection of right-hemisphere areas, shown in Figure 3, in-

cluding SPL, precuneus, cingulate sulcus, insula, lateral oc-

cipital cortex, and V7. We found no areas where NDH

smoothness learning was correlated with FC from our left

IPL seed. Because of the exploratory nature of this analysis,

we do not delve broadly into the scattered areas showing FC

with individual seed regions, though we provide the full data

(including left-hemisphere connectivity, that is, intrahemi-

spheric) in Table 2. Instead, we focus on convergent patterns

of FC across analyses.

We found one convergence across our analyses: the

learning-predictive interhemispheric connectivity from left

M1 (Fig. 2) and left IPS (Fig. 3) both included the same

area in right SPL, as illustrated in Figure 4 and quantiﬁed

in Table 3. The ‘‘intersection volume’’ shared between the

two clusters was 0.31 cm

(46% of the M1-linked cluster,

22% of the IPS-linked cluster).

We found no areas where interhemispheric connectivity

from left-hemisphere seeds predicted NDH baseline, NDH

forgetting, or NDH retention.

Predictive connectivity is stable across training

To determine whether the ‘‘intersection volume’’ in Fig-

ure 4 represented connectivity that is stable across training,

we compared it with our previously identiﬁed network of

areas that show training-related changes in FC from M1 in

either hemisphere (see ﬁg. 4b in Philip and Frey, 2016),

with both maps thresholded at p < 0.001. We found zero

Table 1. Key Behavioral Results from Philip

and Frey 2016 Used for Analysis Here

No. Baseline Learning Forgetting Retention

1 10.00 1.050 0.153 1.096

2 18.41 1.481 0.143 1.337

3 9.08 0.975 0.752 0.213

4 10.68 0.024 — —

5 9.05 0.866 0.170 0.695

6 10.20 0.877 0.449 0.303

7 8.87 0.871 — —

8 10.11 1.039 — —

9 10.10 1.005 0.911 0.093

10 9.72 1.117 0.406 0.683

11 7.59 0.501 0.452 0.049

12 9.67 0.507 0.717 0.831

13 8.38 0.809 0.361 0.428

14 11.49 0.557 0.551 0.062

15 10.66 0.753 0.657 0.828

16 18.34 1.245 0.029 1.216

17 13.35 1.078 0.336 1.414

18 10.00 1.050 0.153 1.096

19 18.41 1.481 0.143 1.337

All values represent movement smoothness. Baseline = 1 * ve-

locity peaks per 45 mm segment; learning, forgetting, and retention

are in Z

NDH

(Z-score relative to baseline day). Missing data = partic-

ipant lost to 6-month follow-up.

NDH, nondominant hand.

FIG. 2. Right-hemisphere areas where FC with left M1 seed predicted NDH smoothness learning. FC, functional connec-

tivity; NDH, nondominant hand. Color images are available online.

312 PHILIP ET AL.

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overlap between voxels in the ‘‘intersection volume’’ and

voxels with training-related changes in M1 connectivity.

Therefore, the pattern of interhemispheric FC that predicted

subsequent NDH learning (Fig. 4) appears to be stable across

time and the learning of relevant motor skills.

Left M1 and IPS project onto right SPL through two

separate networks

Based on our results thus far, it remains unclear whether

the FC from left M1 and left IPS onto right SPL (the ‘‘inter-

section volume’’) reﬂects separate connections or an indirect

connection (e.g., from left M1 to left IPS to right SPL). To

address this, we assessed whether the two seed regions

showed FC with each other. The two seed regions were not

functionally connected: for each seed, the network of signif-

icantly connected voxels did not contain the other seed, as

shown in Supplementary Figure S1. To further address the

separability of M1-related and IPS-related FC, we identiﬁed

the network of areas with differential connectivity to each

seed region. Bilateral somatosensory–motor areas were pref-

erentially connected to left M1, whereas areas involved in

FIG. 3. Right-hemisphere areas where FC with left IPS seed predicted NDH smoothness learning. IPS, intraparietal sulcus.

Color images are available online.

Table 2. Clusters with Learning-Predictive Connectivity with Left-Hemisphere Seeds

Seed Hemisphere Cluster area

Center of gravity

Z-scorexyz

Left M1 Right (interhemispheric) Anterior cingulate 9.1 36.4 13.0 3.34

Inferior frontal gyrus 53.0 25.3 1.7 3.54

Insula 41.6 24.8 2.9 3.65

Putamen/caudate 20.1 2.2 9.9 3.40

Caudate* 19.0 19.0 26.0 4.04

SPL 32.7 56.3 44.6 3.98

Left Parahippocampal gyrus 27.0 3.3 31.4 3.88

Parahippocampal gyrus 13.9 14.2 29.2 3.24

Parahippocampal gyrus 19.7 30.5 9.2 3.50

Insula 38.9 19.6 4.9 3.57

Insula 43.5 5.7 6.9 3.43

Caudate 17.9 13.4 27.3 3.05

Middle frontal gyrus 36.1 12.7 33.2 3.58

Left IPS Right (interhemispheric) Lateral occipital 31.6 84.9 3.8 3.41

Lateral occipital* 31.0 73.0 10.0 3.78

Insula 36.6 2.2 15.3 3.97

SPL 32.4 59.7 41.4 3.50

V7 29.5 71.3 48.0 3.23

Precuneus* 13.0 45.0 48.0 4.68

Left Cerebellum 48.9 64.2 26.4

3.97

Lingual gyrus 20.3 77.5 2.7 4.00

V1d/V2d 5.0 91.3 11.1 3.11

Inferior frontal sulcus 41.8 14.0 24.0 4.08

Cingulate sulcus 12.1 3.5 41.1 3.25

Interhemispheric connectivity (right hemisphere) data are visualized in Figures 2 + 3. Z-score = gaussianized T of correlation between seed

timecourse and participant learning scores, at center of gravity (CoG).

*Values reported for peak instead of CoG due to concave cluster.

IPS, intraparietal sulcus; SPL, superior parietal lobule.

INTERHEMISPHERIC PREDICTION OF LEARNING 313

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attention (including frontal eye ﬁelds, inferior frontal gyrus,

and inferior frontal sulcus) and higher order visuomotor

control (PMd) were preferentially connected to left IPS, as

illustrated in Figure 5. Overall, this suggests that left IPS

and left M1 functionally connect to right SPL as part of dif-

ferent networks.

Post hoc analysis: PMd–PMd interhemispheric

connectivity does not predict NDH learning

After seeing the above results, we performed a post hoc

FC analysis to determine whether PMd–PMd interhemi-

spheric connectivity supported NDH learning, based on pre-

vious results that showed a speciﬁc role of bilateral PMd in

drawing and right PMd in NDH drawing (Potgieser et al.,

2015). Our results did not support the hypothesis that

PMd–PMd interhemispheric connectivity channeled left-

hemisphere information to the right hemisphere: right

PMd (deﬁned as the contralateral homolog of our left PMd

seed) was not among the areas with smoothness-learning-

correlated FC from left PMd, as shown in Supplementary

Figure S2. However, the pattern of functional connec-

tions from the left PMd included an area at the left middle

frontal gyrus (z = 34), which almost coincides with the left

middle frontal gyrus area connected from the left M1 seed

(z = 33.2, Table 2 and Fig. 2). Left PMd was also function-

ally connected to the right-hemisphere homolog of this mid-

dle frontal gyrus area, which could potentially indicate an

indirect contribution of left PMd to interhemispheric interac-

tion through the bilateral middle frontal gyrus.The areas with

smoothness-learning-correlated FC from left PMd included

one voxel in our right SPL ‘‘overlap cluster’’ (<0.01 cm

representing 3% of the SPL cluster with center of gravity

x = 28, y = 54, z = 35.2). However, the left M1 and IPS

seeds were within the network functionally correlated with

the left PMd seed, but not vice versa (Supplementary

Fig. S3); in other words, the apparent FC between left

PMd and right SPL may have arisen from indirect connec-

tions through left M1 and IPS, and possibly including the

bilateral middle frontal gyrus. Given this indirect role, the

overlap’s minute size, and the post hoc nature of this analy-

sis, we did not further interpret the FC between left PMd and

right SPL.

Discussion

We explored FC patterns in the healthy adult human brain,

to identify interhemispheric connectivity that predicted later

PDT skill with the left NDH. We found that subsequent abil-

ity to learn the NDH drawing task was correlated with con-

vergent FC between two of our left-hemisphere seeds

(primary motor cortex hand area, M1; and IPS) and a re-

stricted region of the right SPL. In contrast, we found no in-

terhemispheric connectivity that predicted NDH initial or

maximum performance—only NDH learning (magnitude of

changes across training). This suggests that interhemispheric

connectivity from left-hemisphere areas onto right SPL may

speciﬁcally predict individuals’ ability to learn a precision,

DH-lateralized skill with the NDH.

Model of interhemispheric connectivity to support

NDH skill learning

In typical right-handed adults, the dominant right hand

(and left cerebral hemisphere) is specialized for coordination

of muscle forces across joints, which are critical for feedfor-

ward control and precision smooth movement (Sainburg and

Kalakanis, 2000; Sainburg, 2002). These left-hemisphere spe-

cializations support precision movement with both hands, as

demonstrated by the pattern of bilateral impairments found in

stroke patients with unilateral lesions (Schaefer et al., 2007).

These left-hemisphere specializations are likely to support ip-

silateral NDH movement through interhemispheric cortical

connections, rather than only through uncrossed descending

pyramidal ﬁbers. Interhemispheric transcallosal connections

(Doron and Gazzaniga, 2008) play a role in coordinating bi-

lateral movements (Kennerley et al., 2002), with greater in-

volvement for more difﬁcult tasks (Bueteﬁsch et al., 2014;

Hummel et al., 2003; Wischnewski et al., 2016). This study

provides the ﬁrst model of how interhemispheric connections

Table 3. Overlapping Clusters in Right SPL Show Functional Connectivity from Left-Hemisphere Seeds

Seed region

Volume

(voxels)

Volume

(cm

)

SPL center of gravity SPL peak

xyZxyz

Left M1 83 0.66 32.7 56.3 44.6 29.0 57.0 46.0

Left IPS 183 1.46 32.0 61.3 42.3 33.0 59.0 42.0

Union (either) 227 1.80 32.2 59.6 43.1 33.0 57.0 44.0

Intersection (both) 39 0.31 33.7 57.4 43.8 33.0 57.0 44.0

FIG. 4. Learning-predictive interhemispheric connectivity

from left M1 and left IPS converges on the same area of right

SPL. SPL, superior parietal lobule. Color images are avail-

able online.

314 PHILIP ET AL.

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may recruit left-hemisphere specializations to support the ip-

silateral NDH for learning of a precision feedback-driven

task, as shown in Figure 6.

According to our model in Figure 6, NDH learning is sup-

ported by interhemispheric connectivity from left M1 and/or

IPS onto right SPL. Right M1 must also be involved for

descending corticospinal control of the left NDH (Lawrence

and Kuypers, 1968; Trevarthen, 1965), and SPL-M1 con-

nections have been well established in monkeys and humans

(Chen et al., 2003; Cohen and Andersen, 2002; Dejerine and

Dejerine-Klumpke, 1895; Koch et al., 2007; Mountcastle

et al., 1975). Each of these left-hemisphere areas may be ca-

pable of interhemispheric connections. M1 interhemispheric

connections are better known (Ferbert et al., 1992; Hortoba

gyi et al., 2011; Kobayashi et al., 2003; Meyer et al., 1998;

Pelled et al., 2009), but they do not fully explain interhemi-

spheric connectivity: callosotomy patients show greater loss

of IHC between parietal regions than between primary senso-

rimotor regions (Roland et al., 2017), and we found no cor-

relations between M1-M1 FC and NDH learning.

Each of our three key areas (left M1, left IPS, right SPL)

have well-established roles in control of precision move-

ments. Left M1 has a well-established transcallosal role in

motor control, both direct (Bueteﬁsch et al., 2014; Bundy

et al., 2018; Vines et al., 2008; Wischnewski et al., 2016)

and as a relay (Arai et al., 2011; Fiori et al., 2017; Koch

et al., 2006). Left IPS is a primary candidate for localization

of the lateralized mechanisms for precision movement con-

trol (Mutha et al., 2011), and may serve as a locus for the

processes underlying left/right-hand choices (Fitzpatrick

et al., 2018). Right SPL has been implicated in numerous

mechanisms that support movement control, including top-

down (goal-directed) visuospatial attention (Shomstein

et al., 2010; Wang et al., 2016), integration of multisensory

information for movement (Andersen and Buneo, 2003),

combining and maintaining representations of goal and

movement (Averbeck et al., 2009; Wolpert et al., 1998),

and motor skill learning (Della-Maggiore et al., 2004).

We found that PMd–PMd interhemispheric connectivity

did not predict subsequent NDH learning, despite previous re-

sults that point toward a potential role of PMd–PMd callosal

connectivity in channeling left-hemisphere information to the

right parietal cortex for the control of NDH drawing (Pot-

gieser et al., 2015), as part of PMd’s role in target-directed

visuomotor control of movement and associated connectivity

from ipsilateral parietal cortex (Binkofski et al., 1999; Wise

et al., 1997). Our data suggested that left PMd may lie ‘‘up-

stream’’ of left M1 and IPS at rest, but our hypothesized

model does not encompass the (likely many) sources of FC

FIG. 5. Areas where FC

differed between left M1 seed

and left IPS seed. Z-scores

indicate correlation with

‘‘M1 minus IPS,’’ so positive

values = M1, negative val-

ues = IPS. Color images are

available online.

FIG. 6. Model of interhemispheric connectivity supporting

learning of precision skill with the left NDH.

INTERHEMISPHERIC PREDICTION OF LEARNING 315

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upstream of left M1 and IPS. Regardless, it is unsurprising

that our PMd ﬁndings differed from Potgieser et al.’s

(2015), because the two studies differed in design and goal:

at rest, callosal PMd–PMd connections do not predict NDH

learning, but during task execution, callosal PMd–PMd con-

nections could still play a role in NDH drawing performance.

One complicating feature of our current data is that our

two interhemispheric connections had opposite directions.

For FC from left M1 to right SPL, more connectivity pre-

dicted greater NDH learning; but from left IPS to right

SPL, less connectivity predicted greater NDH learning.

This seems to arise because the two areas are involved in dif-

ferent networks (Fig. 5), not because of inhibitory intercon-

nections between left M1 and left IPS. Therefore, it seems

likely that the left IPS network supports or is correlated

with mechanisms that support the normal dominance of the

DH for precision skill (and thus impairs or is anticorrelated

with the ability to work against the normal pattern of domi-

nance). We see at least two nonexclusive ways that this could

happen: motor and/or attentional. Under the motor explana-

tion, given the role of left IPS in lateralized specializations

(Mutha et al., 2011), connectivity from left IPS could reﬂect

DH-speciﬁc mechanisms’ strength and/or speciﬁcity. Under

the attentional explanation, interhemispheric connectivity

from left IPS could reﬂect the attentional mechanism that

supports the normal bias of attention toward the workspace

and/or affordances accessible to the DH (Colman et al.,

2017; de Bruin et al., 2014). Further explanations are cer-

tainly possible, and additional research will be needed to

characterize the countervailing effects of connectivity from

left M1 versus left IPS.

The FC underlying our model did not overlap with the net-

work of functional connections that change across NDH skill

learning (Philip and Frey, 2016). This suggests that our

model may represent a network that remains stable across

time and experience. If this network is stable at the within-

individual level, its presence or strength could provide a di-

agnostic predictor for the success of therapies that focus on

NDH compensation.

Interhemispheric FC predicted NDH learning,

not NDH performance

We found no interhemispheric mechanisms associated

with NDH performance (baseline or post-training), only

with NDH learning. This ﬁnding could arise from our choice

to use task-free (resting-state) FC MRI to identify interhemi-

spheric mechanisms. Potentially, NDH performance could

involve mechanisms that are only active during the execution

of NDH action. Therefore, we anticipate that future studies

using task MRI will reveal additional mechanisms support-

ing NDH performance.

Limitations

This was a post hoc exploratory study, using a pre-existing

dataset (Philip and Frey, 2016). Additional behavioral out-

comes were tested (maximum performance, baseline per-

formance, and long-term retention), with negative results.

Our ﬁndings for learning of NDH performance (convergent

interhemispheric connectivity on right SPL), while strik-

ing, should be conﬁrmed in an apriorihypothesis-driven

study.

Conclusions

In healthy right-handed adults, we found that the ability

tolearnaPDTwiththeNDHwaspredictedbyinterhemi-

spheric connectivity from the left primary motor cortex

and left IPS onto a small cluster in the right SPL. To our

knowledge, this provides the ﬁrst anatomical hypothesis

of the interhemispheric mechanisms that support the ability

to learn a precision skill (i.e., a traditionally DH skill) with

the NDH.

If future studies can conﬁrm this mechanism, it provides

anatomical targets for noninvasive brain stimulation or

brain–machine interfaces to facilitate these mechanisms,

and thereby facilitate the acquisition of NDH skill for indi-

viduals with chronic impairment or loss of the dominant

right hand.

Authors’ Contributions

B.A.P. contributed to conceptualization, methodology,

software, investigation, writing—original draft, writing—

review & editing, visualization, funding acquisition. M.M.

contributed to methodology, software, formal analysis,

writing—review & editing, visualization. S.H.F. contributed

to conceptualization, resources, writing—reviewing & edit-

ing, funding acquisition, supervision.

Acknowledgments

The authors thank Kelli Buchanan, Drew Hensel, and

Chris Saville for their assistance with data collection.

Author Disclosure Statement

B.A.P. reports a licensing agreement with PlatformSTL to

commercialize the precision drawing task, outside the sub-

mitted work.

Funding Information

This work was supported by the National Institute for

Neurological Disorders and Stroke at the National Institutes

of Health (grant number NS083377) to S.H.F., and by the

Program in Occupational Therapy at Washington University

in St. Louis to B.A.P.

Supplementary Material

Supplementary Figure S1

Supplementary Figure S2

Supplementary Figure S3

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Address correspondence to:

Benjamin A. Philip

Program in Occupational Therapy

Washington University School of Medicine

Campus Box 8505

4444 Forest Park Avenue

St. Louis, MO 63108

USA

E-mail: [email protected]

318 PHILIP ET AL.

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