研究
Functional connectivity–based prediction of global
cognition and motor function in riluzole-naive
amyotrophic lateral sclerosis patients
Luqing Wei1, Chris Baeken2,3,4, Daihong Liu5, Jiuquan Zhang5, and Guo-Rong Wu6
1School of Psychology, Jiangxi Normal University, Nanchang, 中国
2Ghent Experimental Psychiatry Lab, Department of Head and Skin, UZ Gent/ Universiteit Gent, Ghent, 比利时
3Department of Psychiatry, UZ Brussel/ Free University of Brussels, 布鲁塞尔, 比利时
4Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, 荷兰
5放射科, Chongqing University Cancer Hospital, Chongqing, 中国
6Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, 中国
开放访问
杂志
关键词: Amyotrophic lateral sclerosis, Cognitive changes, Functional connectivity, Motor
severity
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抽象的
Amyotrophic lateral sclerosis (ALS) is increasingly recognized as a multisystem disorder
accompanied by cognitive changes. 迄今为止, no effective therapy is available for ALS patients,
partly due to disease heterogeneity and an imperfect understanding of the underlying
pathophysiological processes. Reliable models that can predict cognitive and motor deficits are
needed to improve symptomatic treatment and slow down disease progression. This study
aimed to identify individualized functional connectivity–based predictors of cognitive and
motor function in ALS by using multiple kernel learning (MKL) regression. Resting-state fMRI
scanning was performed on 34 riluzole-naive ALS patients. Motor severity and global cognition
were separately measured with the revised ALS functional rating scale (ALSFRS-R) 和
Montreal Cognitive Assessment (MoCA). Our results showed that functional connectivity within
the default mode network (DMN) as well as between the DMN and the sensorimotor network
(SMN), fronto-parietal network (FPN), and salience network (SN) were predictive for MoCA
scores. 此外, the observed connectivity patterns were also predictive for the individual
ALSFRS-R scores. Our findings demonstrate that cognitive and motor impairments may share
common connectivity fingerprints in ALS patients. 此外, the identified brain connectivity
signatures may serve as novel targets for effective disease-modifying therapies.
作者总结
Amyotrophic lateral sclerosis is recognized as a multisystem disorder, and currently no effective
therapy is available for this devastating disease. Reliable models that can predict disease
progression may facilitate the development of more efficient symptomatic treatment. This study
used multiple kernel learning algorithm to identify a potential functional connectivity–based
marker for cognitive and motor functioning in ALS. The results show that cognitive decline and
motor progression could be predicted by seed-based functional connectivity from the medial
prefrontal cortex/posterior cingulate cortex to the sensorimotor network, 额顶叶
网络, and salience network. The identified brain connectivity signatures may serve as novel
targets for effective disease-modifying therapies.
引文: Wei, L。, Baeken, C。, 刘, D .,
张, J。, & 吴, G.-R. (2022).
Functional connectivity–based
prediction of global cognition and
motor function in riluzole-naive
amyotrophic lateral sclerosis patients.
网络神经科学, 6(1), 161–174.
https://doi.org/10.1162/netn_a_00217
DOI:
https://doi.org/10.1162/netn_a_00217
支持信息:
https://doi.org/10.1162/netn_a_00217
已收到: 22 七月 2021
公认: 17 十一月 2021
利益争夺: 作者有
声明不存在竞争利益
存在.
Corresponding Authors:
Jiuquan Zhang
zhangjq_radiol@foxmail.com
Guo-Rong Wu
guorongwu@swu.edu.cn
处理编辑器:
Martijn van den Heuvel
版权: © 2021
麻省理工学院
在知识共享下发布
归因 4.0 国际的
(抄送 4.0) 执照
麻省理工学院出版社
Predicting cognitive and motor function in ALS patients
介绍
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progres-
sive degeneration of lower motor neurons in the spinal cord and brainstem, and upper motor
neurons in the motor cortex. Although the degenerative process predominantly affects the
motor system, cognitive deficits have been described as well (戈德斯坦 & Abrahams, 2013;
Phukan et al., 2007). Cognitive dysfunction, as highlighted in a recent study, can adversely
impact patient compliance with treatment and quality of life (Huynh et al., 2020).
现在, no effective therapy is available for ALS patients, partly because of disease het-
erogeneity and an imperfect understanding of the pathophysiological processes (Kiernan et al.,
2021). Further improvements in symptom management depend on advances in the under-
standing of the origins and progression of this devastating neurological disorder (Hardiman
等人。, 2011; Kiernan et al., 2021). The development of novel biomarkers to objectively assess
disease progression may refine the therapeutic trial design, thereby facilitating the translation
of novel therapies into the ALS clinic (Kiernan et al., 2011, 2021).
According to former research, motor and cognitive impairments in ALS are associated with
pathological lesions of motor networks and a progressive spread to extramotor cortical and
subcortical areas (Braak et al., 2013; Brettschneider et al., 2013). 有趣的是, functional
MRI (功能磁共振成像) has proven to be sensitive to the detection of inherent cerebral motor and extra-
motor pathology of ALS (Turner et al., 2011). 尤其, resting-state fMRI (rs-fMRI) 提供
a new research method to explore ALS as a system failure of interconnected networks (车工
等人。, 2011). 例如, ALS patients—relative to healthy controls—showed functional con-
nectivity (FC) abnormalities in the sensorimotor network (SMN), default mode network (DMN),
fronto-parietal network (FPN), and salience network (SN) (Agosta et al., 2011; Douaud et al.,
2011; Mohammadi et al., 2009; Tedeschi et al., 2012; Trojsi et al., 2015). 而且, FC
changes in these networks correlated with motor severity or cognitive test scores (Agosta
等人。, 2011, 2013; Trojsi et al., 2015). Previous rs-fMRI studies on ALS patients primarily
focused on revealing FC differences at a group-wise level or employing univariate analytical
techniques to identify brain–behavior relationships. 然而, FC patterns are reported to be
unique for individuals (Finn et al., 2015) and could be used as a predictor for clinical variables
(例如, symptom severity and treatment outcome) in neurological and psychiatric disorders (例如,
Alzheimer’s disease and major depressive disorder) (Ju et al., 2020; 莱克等人。, 2019; 林等人。,
2018; Yip et al., 2019). The accumulating evidence suggests that FC patterns can also serve as
predictors of disease evolution of ALS at the individual level.
The aim of the current study in ALS was to identify individualized FC-based predictors for
cognitive and motor function by using machine learning–based approaches. Compared to uni-
variate analytical techniques, the machine learning models could protect against overfitting by
testing brain–behavior relationships in a novel sample and provide a neuroimaging signature
with high potential for clinical translation (Jiang et al., 2018; Shen et al., 2017; Yip et al.,
2019). Given that previous studies on ALS patients highlighted connectivity abnormalities in
the SMN, DMN, FPN, and SN (Agosta et al., 2011; Douaud et al., 2011; Mohammadi et al.,
2009; Tedeschi et al., 2012; Trojsi et al., 2015), FC patterns within and between these canon-
ical networks were chosen for the machine learning model. Especially, the core systems of the
DMN, the medial prefrontal cortex (MPFC) and posterior cingulate cortex (PCC) (Andrews-
Hanna, 2012; Fransson & Marrelec, 2008) were selected as regions of interest (ROIs), 基于
on their vital roles in the pathogenesis of cognitive impairment (Anticevic et al., 2012;
Binnewijzend et al., 2012; Ding et al., 2014; Pardo et al., 2007). We hypothesized that FC
patterns from the MPFC and PCC to the remaining regions of the SMN, DMN, FPN, and SN
网络神经科学
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Predicting cognitive and motor function in ALS patients
would contribute to the prediction of global cognitive functioning in ALS. 此外, 自从
ALS-specific cognitive changes are coupled with more severe motor decline (Chiò et al., 2019;
Crockford et al., 2018; Elamin et al., 2013), we also tested whether the FC model for global
cognition can be extrapolated to the prediction of motor progression in ALS disease.
材料和方法
Subjects and Cognitive Testing
Thirty-four ALS patients (21 male and 11 女性) used in this study were recruited from the
Department of Neurology at Southwest Hospital (Chongqing, 中国). Based on the revised El
Escorial criteria (Brooks et al., 2000), these patients were diagnosed with sporadic probable
or definite ALS. Severity of motor impairment was measured with the revised ALS functional
rating scale (ALSFRS-R) (Cedarbaum et al., 1999). The range of ALSFRS-R scores was 21 到 45
in this work (10 patients at the range of 40–48, 18 patients at the range of 30—39, 和 6 患者
at the range of 20–29). Disease duration was defined as the duration from symptom onset to the
scanning date (月), and disease progression rate was defined as (48-ALSFRS-R)/(疾病
duration) (Kimura et al., 2006). Exclusion criteria included: (1) the presence of other neurolog-
ical or psychiatric disorders; (2) clinical diagnosis of frontotemporal dementia (Neary et al.,
1998); 和 (3) family history of motor system diseases. None of the patients received riluzole
therapy before.
The Mini-Mental State Examination (MMSE) (Folstein et al., 1975) and Montreal Cognitive
Assessment (MoCA) (Nasreddine et al., 2005) were applied to evaluate global cognition in ALS
Patients. Two subjects missed the MMSE and MoCA data, 和 32 patients were used for the
prediction of global cognitive functioning. All patients were right-handed based on measure-
ments of the Edinburgh inventory. Study protocols were evaluated and approved by the Med-
ical Research Ethics Committee of the Southwest Hospital. In accordance with the Helsinki
Declaration, all participants provided written informed consent. The detailed demographic
and clinical characteristics are presented in Table 1.
ALSFRS-R:
A scale measured the severity of
motor impairment.
Riluzole:
A medication is used to treat ALS,
which may delay disease progression
and prolong life.
MoCA:
A scale applied to evaluate global
cognitive function.
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桌子 1. Demographic and clinical data of ALS patients
年龄
Male/female
Education level (年)
Onset (limb/bulbar/both)
El Escorial criteria (probable/definite)
Disease duration (月)
Disease progression rate
ALSFRS-R score
MoCA score
MMSE score
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48.2 ± 11.5
22/12
6.9 ± 3.0
26/8/0
13/21
24.8 ± 26.7
1.15 ± 1.2
34.4 ± 7.0
25.1 ± 3.1
28.2 ± 2.1
笔记. ALSFRS-R, revised ALS functional rating scale; MoCA, Montreal Cognitive Assessment; MMSE, Mini-
Mental State Examination.
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Predicting cognitive and motor function in ALS patients
Data Acquisition
Image data were acquired using a Siemens 3T Tim Trio scanner. Functional images were
acquired using an echo-planar imaging sequence with the following settings: TR/ TE =
2,000 ms/30 ms, flip angle = 90°, FOV = 192 × 192 mm2, matrix = 64 × 64, slices = 36,
thickness/gap = 3 mm/1 mm, voxel size = 3 × 3 × 3 mm3. For each subject, a total of 240
volumes were acquired during which participants were instructed to keep their eyes closed but
stay awake for a period of 480 s. High-resolution anatomical images were obtained using a
magnetization-prepared rapid gradient-echo (MP-RAGE) sequence with the following settings:
TR/TE = 1,900 ms/2.52 ms, flip angle = 9°, 矩阵大小= 256 × 256, slices = 176, thickness =
1 毫米, and voxel size = 1 × 1 × 1 mm3.
Data Preprocessing
Functional images preprocessing was performed using fMRIPrep (version 1.4.1) (Esteban
等人。, 2019). Briefly, the T1-weighted (T1w) image was corrected for intensity nonuniformity
with N4BiasFieldCorrection (ANTs) and used as T1w-reference throughout the workflow.
然后, a blood oxygen level–dependent (大胆的) reference volume and its skull-stripped
version were generated using a custom methodology of fMRIprep. The BOLD reference
was coregistered with the T1w reference (bbregister, FreeSurfer). Coregistration was config-
ured with 9 degrees of freedom to account for the distortions remaining in the BOLD ref-
erence. Head-motion parameters with respect to the BOLD reference are estimated before
any spatiotemporal filtering using mcflirt (FSL). After that, the BOLD runs were slice-time
corrected using 3dTshift (AFNI) and resampled into the MNI152NLin2009cAsym standard
volumetric space. Framewise displacement (FD) (Power et al., 2012) was calculated for
each functional run.
To remove the confounds due to physical and physiological noise, the following nuisance
regressors were simultaneously included in a linear regression model: six realignment param-
eters and their temporal derivatives (Power et al., 2012), physiological noise estimated using
the anatomical component correction method (aCompCor, the top five principal components
from the union of cerebrospinal fluid and white matter masks calculated in T1w space)
(Behzadi et al., 2007), and first-order Legendre polynomial. 最后, temporal band-pass
filtering (0.01 ～ 0.1 赫兹) was applied to the residual time series. For further details of the
pipeline, please see the section corresponding to the workflows in fMRIPrep’s documentation
(https://fmriprep.org/en/latest/workflows.html).
Regions of Interest–Based Functional Connectivity Analyses
On the basis of former research (Raichle, 2011), the supplementary motor area (SMA) 和
bilateral motor cortex constituted the SMN, the MPFC, PCC, and bilateral parietal cortex con-
stituted the DMN, and the dorsal medial prefrontal cortex (全氟碳化物), bilateral anterior PFC, 和
superior parietal cortex constituted the FPN. For the SN, it comprised five key nodes (IE。, dor-
sal anterior cingulate cortex [dACC], bilateral anterior PFC and insula) as described in Menon
(2015). Especially, the MPFC and PCC were chosen as two seed regions based on their crucial
role in cognitive decline. All above-mentioned brain regions and corresponding coordinates
(桌子 2) used in this work were obtained from Raichle (2011). For each individual subject, 我们
computed the Pearson correlation coefficient between the averaged time course of each seed
地区 (sphere with radius = 5 毫米) and the time courses of the remaining network’s nodes.
Then the generated Pearson’s r maps were converted to z maps using Fisher’s z transformation
网络神经科学
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Predicting cognitive and motor function in ALS patients
桌子 2.
Brain regions and corresponding coordinates applied for FC analyses
Network/region
SMN
SMA
Left motor cortex
Right motor cortex
DMN
MPFC
PCC
Left parietal cortex
Right parietal cortex
FPN
dmPFC
Left anterior PFC
Right anterior PFC
Left superior parietal cortex
Right superior parietal cortex
SN
dACC
Left anterior PFC
Right anterior PFC
Left insula
Right insula
MNI coordinates
0, −21, 48
−39, −26, 51
38, −26, 48
−1, 54, 27
0, −52, 27
−46, −66, 30
49, −63, 33
0, 24, 46
−44, 45, 0
44, 45, 0
−50, −51, 45
50, −51, 45
0, 21, 36
−35, 45, 30
32, 45, 30
−41, 3, 6
41, 3, 6
笔记. SMA, supplementary motor area; MPFC, medial prefrontal cortex; PCC, posterior cingulate cortex;
dmPFC, dorsal medial prefrontal cortex; 全氟碳化物, 前额皮质; dACC, dorsal anterior cingulate cortex.
(Figure 1A and 1B). The above FC analyses were performed using the rsHRF toolbox (吴
等人。, 2021) (https://www.nitrc.org/projects/rshrf).
Individualized Prediction
We employed a multiple kernel learning (MKL) with lasso regularization algorithm to predict
cognitive (MMSE and MoCA) and motor (ALSFRS-R) test scores, implemented in the PRoNTo
(https://www.mlnl.cs.ucl.ac.uk/pronto, version 2.1.1) and SpicyMKL toolbox (https://ibis.t.u
-tokyo.ac.jp/suzuki/software/SpicyMKL/, version 3) (Suzuki & Tomioka, 2011). A nested
cross-validation was used simultaneously for the selection of the hyperparameter (5-fold
cross-validation for the inner loop to optimize the model’s hyperparameter, 那是, grid search
soft-margin C: [0.0001, 0.001, 0.01, 0.1, 1, 10, 100], then the best C value was used for the
Nested cross-validation:
Training model hyperparameters and
evaluating model performance.
网络神经科学
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Predicting cognitive and motor function in ALS patients
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(A) Functional connectivity maps (left panel) and their relationships (right panel) with Montreal Cognitive Assessment (MoCA) (左边
数字 1.
part matrix) and amyotrophic lateral sclerosis functional rating scale (ALSFRS-R) scores (right part matrix), obtained by partial correlation anal-
分析 (with age, 性别, and mean framewise displacement as covariates). (乙) Functional connectivity (FC) features for the prediction of MoCA
and ALSFRS-R scores. (C) Flow chart of the nested cross-validation. (D) Scatter plot showing actual and predicted MoCA (r = 0.737, p = 0.015) /
ALSFRS-R (r = 0.764, p = 0.003) scores. The size of the scatter point is proportional to age.
网络神经科学
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Predicting cognitive and motor function in ALS patients
Meng’s Z test:
Comparison of the correlated
correlation coefficients.
Computational lesion analysis:
Examining the specificity of
connectivity for predictions.
outer loop) and assessment of generalization capacity (leave-one-out for the outer loop, 看
Figure 1C). 年龄, 性别, and mean FD were combined in one single linear kernel. For each
FC, a linear kernel was computed. All kernels were mean centered and normalized before
MKL modeling. The Pearson correlation coefficient (r) and root mean squared error (rMSE)
were calculated between the actual and predicted motor and cognitive test scores for the
overall predictive performance. Permutation tests were carried out to assess the statistical sig-
nificance of the correlation coefficient and rMSE (randomly shuffled cognitive and motor
scores 1,000 次). The results were considered significant if the p value < 0.05/3 = 0.017
(Bonferroni correction to account for multiple comparisons).
To examine the specificity of the SMN, DMN, FPN, and SN in predicting cognitive and
motor test scores, the visual (left visual system: −7, 83, 2; right visual system: 7, 83, 2) and
auditory networks (left auditory system: −62, −30, 12; right auditory system: 59, −27, 15) that
are not directly related to ALS disease pathology were also included in the machine learning
model (Raichle, 2011). The predictive power of the model with and without the two networks
was compared using Meng’s Z test (Meng et al., 1992).
Besides, to further clarify the specificity of FC from the seed regions to the FPN or SN or SMN
in the prediction of individual cognitive and motor scores, a computational lesion analysis was
further performed (Feng et al., 2018; Wang et al., 2021). Concretely, the FC patterns excluding
connectivity from seed regions to the FPN or SN or SMN were employed for the prediction of
MoCA and ALSFRS-R scores. The predictive power for lesion and no lesion model was com-
pared using Meng’s Z test.
RESULTS
The MKL regression model showed that FC from the MPFC and PCC to the DMN regions
(i.e., bilateral parietal cortex) as well as to the SMN, FPN, and SN regions were predictive for
the individual MoCA and ALSFRS-R scores. As shown in Figure 1D, the predicted MoCA
and ALSFRS-R scores significantly correlated with the actual MoCA (r = 0.737, p = 0.015;
rMSE = 2.172, p = 0.014) and ALSFRS-R (r = 0.764, p = 0.003; rMSE = 5.006, p = 0.006)
scores. No prediction was found for the MMSE scores (r = 0.013, p = 0.573; rMSE = 5.746,
p = 0.438).
After inclusion of the visual and auditory networks in the MKL model, FC patterns were
not predictive for the MoCA (r = 0.369, p = 0.123; rMSE = 3.307, p = 0.086), MMSE (r =
0.203, p = 0.291; rMSE = 3.726, p = 0.442), and ALSFRS-R scores (r = 0.309, p = 0.157;
rMSE = 9.057, p = 0.160). Moreover, the predictive power of the model for MoCA and
ALSFRS-R scores decreased dramatically (MoCA: Meng’s Z = 2.637, p = 0.004; ALSFRS-R:
Meng’s Z = 3.317, p < 0.001). These results may indicate that the SMN, DMN, FPN, and
SN are specific to the individualized prediction of cognitive and motor functions in ALS
patients.
The computational lesion analysis revealed that FC patterns excluding connectivity with the
SMN were not predictive for ALSFRS-R (r = 0.152, p = 0.286; rMSE = 15.063, p = 0.226;
Meng’s Z = −3.523, p < 0.001) and MoCA (r = 0.187, p = 0.295; rMSE = 6.756, p =
0.168; Meng’s Z = −3.740, p < 0.001) scores. After exclusion of connectivity to the FPN,
FC patterns were not predictive for ALSFRS-R scores (r = −0.267, p = 0.881; rMSE =
15.946, p = 0.736). Additionally, FC after lesion of the connectivity with SN was not predictive
for MoCA scores (r = −0.015, p = 0.553; rMSE = 8.631, p = 0.842). More details about the
lesion results are presented in Table 3.
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Predicting cognitive and motor function in ALS patients
Table 3.
Results of prediction with specific lesion analysis
Lesion
Seeds-SMN
Dependent variable
MoCA
ALSFRS-R
Seeds-FPN
MoCA
Seeds-SN
ALSFRS-R
MoCA
ALSFRS-R
a Permutation test.
b Parametric test; seeds: MPFC&PCC.
Predictive power
rMSE
6.756
15.063
2.170
15.946
8.631
8.788
p valuea
0.168
0.226
0.002
0.736
0.842
0.036
r
0.187
0.152
0.783
−0.267
−0.015
0.503
p valuea
0.295
0.286
0.002
0.881
0.553
0.017
Lesion vs. no lesion model
Meng’s Z
p valueb
−3.740
<0.001
−3.523
0.576
−4.729
−3.849
−2.322
<0.001
0.718
<0.001
<0.001
0.010
DISCUSSION
This is the first brain imaging study applying the FC-based machine learning algorithm to pre-
dict cognitive and motor function in ALS disease at single-subject level. The MKL model has
identified FC within the DMN as well as between the DMN and the SMN, FPN, and SN, con-
tributing to the prediction of global cognitive functioning in ALS. In addition, the observed FC
patterns also predicted individual motor impairment scores. The current findings show that
individual differences in baseline connectivity within and between large-scale neural networks
contribute to variability in global cognition and motor progression in ALS disease. Further-
more, the identified predication models may provide novel biomarkers for clinical trial
designs, holding the promise of the development of effective therapies for ALS patients.
Individual differences in MoCA scores are closely linked to connectivity from the MPFC
and PCC to the bilateral parietal cortex, demonstrating that the DMN intranetwork connectiv-
ity was predictive for global cognitive function in ALS. The DMN, characterized by deactiva-
tion during goal-directed cognitive tasks and increased activity in self-referential processing
(Buckner et al., 2008; Raichle et al., 2001), plays an important role in the pathogenesis of
cognitive impairment (Binnewijzend et al., 2012; Greicius et al., 2004; Sorg et al., 2007). In
particular, the MPFC and PCC were key structures for cognitive decline, typically found in
normal aging (Pardo et al., 2007), as well as in mild cognitive impairments, and in Alzheimer’s
disease (AD) (Binnewijzend et al., 2012). Previous studies have linked impaired connectivity
within the DMN to ALS patients without dementia (Agosta et al., 2013; Mohammadi et al.,
2009; Trojsi et al., 2015). Moreover, connectivity abnormalities in the DMN correlated with
cognitive performance scores in ALS (Agosta et al., 2013). The current results further lend sup-
port to the assumption that ALS can be characterized by the alteration of functional networks
associated with global cognition, even before the occurrence of overt dementia. As discussed
above, the DMN frequently underpins the development of cognitive deficits in ALS disease.
However, clinical disability scores (ALSFRS-R) could also be reliably predicted by DMN con-
nectivity. As the correlation between DMN connectivity and motor severity has been observed
in patients with ALS (Agosta et al., 2013), our results extended previous findings by showing
that DMN connectivity was predictive for motor progression in newly diagnosed drug-naive
individuals. According to former ALS research, cognitive impairments are associated with a
more rapid decline of motor function (Chiò et al., 2019; Crockford et al., 2018; Elamin
et al., 2013). This could indicate that neural networks accounting for cognitive functioning
may predict motor progression in ALS. Overall, the current findings imply that the pathological
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Predicting cognitive and motor function in ALS patients
process of ALS involves DMN regions, and lesions of the DMN can be predictive for the level
of cognitive and motor decline.
FC between the DMN and SMN, FPN, and SN were predictive for MoCA and ALSFRS-R
scores in ALS, providing further evidence for the involvement of the SMN, FPN, and SN in
ALS. The SMN, FPN, and SN have been described in previous rs-fMRI studies on ALS (Agosta
et al., 2011, 2013; Mohammadi et al., 2009; Tedeschi et al., 2012; Trojsi et al., 2015). In par-
ticular, the SMN is responsible for motor function, and abnormal connectivity in this network
contributes to motor dysfunction in patients with ALS (Agosta et al., 2011, 2013; Mohammadi
et al., 2009; Tedeschi et al., 2012; Trojsi et al., 2015). The FPN subserves attention, executive
processing, planning, and working memory (Corbetta & Shulman, 2002). Altered FPN connec-
tivity could explain the executive deficits frequently observed in ALS (Agosta et al., 2013;
Tedeschi et al., 2012; Trojsi et al., 2015). The SN has been conceptualized as a bottom-up
processor of salient experiences, and is involved in the initiation of cognitive control by
influencing activation of the FPN and the DMN (Menon, 2011; Menon & Uddin, 2010; Seeley
et al., 2007). Abnormal connectivity in the SN accounts for behavioral disturbances (e.g., apa-
thy, irritability, aggression, disinhibition, and distractibility) in ALS patients (Phukan et al.,
2007; Trojsi et al., 2015). Taken together, our current findings suggest that motor and extra-
motor networks are involved in ALS, supporting the notion of ALS as a multinetwork disorder.
Of note, this study showed that FC between the DMN and SMN as well as between the
DMN, FPN, and SN were predictive for MoCA and ALSFRS-R scores, indicating that baseline
internetwork connectivity can be used to explain individual differences in global cognitive and
motor function in ALS patients. As described above about the role of the DMN and SMN, it is
not surprising that global cognition and motor progression can be predicted by using the
connectivity between the DMN and SMN. Interestingly, after the lesion of this internetwork
connectivity, the predictive power of the model for MoCA and ALSFRS-R scores decreased
significantly (i.e., FC patterns were not predictive for these two measurements). This result
may demonstrate that the DMN-SMN connectivity is specific to the individualized prediction
of cognitive and motor functioning in ALS.
Regarding the DMN, FPN, and SN, a triple network model proposes that functional inter-
actions among the three core neurocognitive networks are crucial for attention control, work-
ing memory, decision-making, and other higher level cognitive functions (Chen et al., 2013;
Menon, 2011). Dysfunctional couplings between the three networks may underlie the progres-
sion of cognitive deficits in Parkinson’s disease (Putcha et al., 2016). We found that functional
couplings between the DMN, FPN, and SN were predictive for global cognition in ALS
patients, further supporting the triple network model as a common neuronal substrate of
cognitive functioning. Besides, these couplings were also predictive for individual motor
deficits, suggesting that neural circuits underlying cognitive function may predict motor symp-
toms in ALS.
Although the MoCA scores can be predicted by interactions between the DMN, FPN, and
SN, the computational lesion results have shown the specificity of the DMN-SN coupling
in predicting global cognition of ALS. To support complex and flexible cognitive processes,
the SN signals the DMN to reduce its activity when a salient event is detected (Menon &
Uddin, 2010; Sridharan et al., 2008). Communications between the SN and DMN are crucial
for efficient cognitive control (Bonnelle et al., 2012; Ham et al., 2013; Jilka et al., 2014).
Moreover, successful cognition in elderly people relies on healthy coupling between the SN
and DMN (Putcha et al., 2016; Tsvetanov et al., 2016), and the abnormality of this internet-
work connectivity is associated with cognitive decline in AD patients (He et al., 2014). The
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Predicting cognitive and motor function in ALS patients
above-mentioned findings indicate that the DMN-SN coupling is important for the mainte-
nance of cognitive capacities. Therefore, FC patterns after a lesion of this connectivity were
not predictive for global cognition in ALS. Nonetheless, the DMN-FPN coupling did not show
resembling specificity in predicting MoCA scores. The cooperation between the DMN and
FPN has been shown to control executive functions such as cognitive flexibility, attention,
and working memory (Cole et al., 2012; Dajani & Uddin, 2015; Douw et al., 2016). The spec-
ificity of the DMN-FPN connectivity in predicting cognitive function of ALS should be further
validated by using more specific cognitive measures (e.g., verbal fluency, attention, set shift-
ing, and episodic memory) (Chiò et al., 2019; Phukan et al., 2007), since the MoCA is a gen-
eral and brief cognitive screening instrument. Finally, the specificity was observed for the
DMN-FPN coupling in predicting ALSFRS-R scores, implying that neural networks accounting
for cognitive changes may be used to predict motor decline. A possible explanation for this
finding could be that cognitive changes are coupled with greater motor decline in ALS (Chiò
et al., 2019; Crockford et al., 2018; Elamin et al., 2013).
It should be pointed out that our prediction model was not validated in another indepen-
dent dataset. However, the results with the holdout method indicated that FC within the DMN
as well as between the DMN and the SMN, FPN, and SN could reliably predict global cog-
nition and motor function in ALS patients (Supporting Information Figure S1, unlike the
leave-one-out cross-validation, the test set is never used as the training set and vice versa).
In addition, there is a lack of longitudinal data addressing the predictive model of progression
rate on MoCA and ALSFRS-R scores. Notwithstanding, here we applied the disease progres-
sion rate—defined as (48-ALSFRS-R)/(disease duration)—(Kimura et al., 2006) for MKL model-
ing. The results showed that disease progression rate could not be predicted by the observed
FC patterns (r = 0.403, p = 0.085; rMSE = 1.687, p = 0.074), suggesting that the FC model of
global cognition cannot be extrapolated to the prediction of this measurement. More suitable
FC models are needed to further verify the current finding. Furthermore, future studies with
longitudinal datasets would be necessary to further predict cognitive and motor decline in
ALS patients. Finally, this work only assessed global cognitive function in ALS patients. To
further investigate FC-based predictive models for specific cognitive impairments, a compre-
hensive battery of neuropsychological tests encompassing language, memory, executive func-
tion, social cognition, and visuospatial function should be included in a future study (Chiò
et al., 2019; Consonni et al., 2018; Crockford et al., 2018; Lule et al., 2018).
CONCLUSIONS
In summary, our results show that individual differences in FC within and between large-scale
neural networks contribute to variability in global cognition and motor progression, supporting
the notion of ALS as a multinetwork disorder. Moreover, the identified brain connectivity sig-
natures may provide novel biomarkers for effective therapy of ALS patients. Replication and
validation using other and larger datasets are needed before these models can be confiden-
tially used in clinical practice.
SUPPORTING INFORMATION
Supporting information for this article is available at https://doi.org/10.1162/netn_a_00217.
AUTHOR CONTRIBUTIONS
Luqing Wei: Methodology; Writing – original draft; Writing – review & editing. Chris Baeken:
Validation; Writing – review & editing. Daihong Liu: Data curation; Investigation. Jiuquan
Network Neuroscience
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Predicting cognitive and motor function in ALS patients
Zhang: Conceptualization; Project administration; Supervision. Guo-Rong Wu: Methodology;
Writing – original draft; Writing – review & editing.
FUNDING INFORMATION
Luqing Wei, National Natural Science Foundation of China (https://dx.doi.org/10.13039
/501100001809), Award ID: 31900764. Guo-Rong Wu, National Natural Science Foundation
of China (https://dx.doi.org/10.13039/501100001809), Award ID: 61876156. Jiuquan Zhang,
National Natural Science Foundation of China (https://dx.doi.org/10.13039/501100001809),
Award ID: 82071883. Chris Baeken, Research Foundation - Flanders (FWO), Award ID:
T000720N.
This work was also supported by the Queen Elisabeth Medical Foundation for Neurosci-
ences, by the Ghent University Multidisciplinary Research Partnership “The Integrative Neu-
roscience of Behavioral Control,” a grant BOF16/GOA/017 for a Concerted Research Action of
Ghent University, and by an Applied Biomedical (TBM) grant of the Agency for Innovation
through Science and Technology (IWT), part of the Research Foundation - Flanders (FWO)
PrevenD Project 2.0 (T000720N) and FWO project G011018N.
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