To evaluate the neural basis of subjective fatigue in subjects with multiple sclerosis (MS) using a connectionist framework.
Methods:
Seventy seven subjects with relapsing–remitting MS were recruited in the study and underwent subjective fatigue evaluations and a diffusion MRI scan. Firstly, local white matter Fractional Anisotropy values were correlated with subjective fatigue scores using a voxel-wise approach. The long-range loss of connectivity due to structural damage in the white matter voxels thus associated with subjective fatigue was then assessed using the Network Modification (NeMo) package.
Results:
A voxel-wise regression analysis with fatigue scores revealed a significant association between structural damage and fatigue levels in two discrete white matter clusters, both included in the left cingulate bundle. The connectivity analysis revealed that damage in these clusters was associated with loss of structural connectivity in the anterior and medial cingulate cortices, dorsolateral prefrontal areas and in the left caudate.
Discussion:
Our data point to the cingulum bundle and its projections as the key network involved in subjective fatigue perception in MS. More generally, these results suggest the potential of the connectionist framework to generate coherent models of the neural basis of complex symptomatology in MS.
TedeschiGDinacciDLavorgnaL. Correlation between fatigue and brain atrophy and lesion load in multiple sclerosis patients independent of disability. J Neurol Sci2007; 263: 15–19.
3.
CalabreseMRinaldiFGrossiP. Basal ganglia and frontal/parietal cortical atrophy is associated with fatigue in relapsing-remitting multiple sclerosis. Mult Scler2010; 16: 1220–1228.
4.
GobbiCRoccaMARiccitelliG. Influence of the topography of brain damage on depression and fatigue in patients with multiple sclerosis. Mult Scler2014; 20: 192–201.
5.
PardiniMBonzanoLMancardiGL. Frontal networks play a role in fatigue perception in multiple sclerosis. Behav Neurosci2010; 124: 329–336.
6.
PellicanoCGalloALiX. Relationship of cortical atrophy to fatigue in patients with multiple sclerosis. Arch Neurol2010; 67: 447–453.
7.
YaldizliOGlasslSSturmD. Fatigue and progression of corpus callosum atrophy in multiple sclerosis. J Neurol2011; 258: 2199–2205.
8.
RoccaMAParisiLPaganiE. Regional but not global brain damage contributes to fatigue in multiple sclerosis. Radiology2014: 140417. [Epub ahead of print].
9.
GobbiCRoccaMPaganiE. Forceps minor damage and co-occurrence of depression and fatigue in multiple sclerosis. Mult Scler2014. [Epub ahead of print].
10.
CataniMDell’acquaFBizziA. Beyond cortical localization in clinico-anatomical correlation. Cortex2012; 48: 1262–1287.
11.
KurtzkeJF.Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology1983; 33: 1444–1452.
12.
FlacheneckerPKumpfelTKallmannB. Fatigue in multiple sclerosis: A comparison of different rating scales and correlation to clinical parameters. Mult Scler2002; 8: 523–526.
13.
MarrieRAMillerDMCheluneGJ. Validity and reliability of the MSQLI in cognitively impaired patients with multiple sclerosis. Mult Scler2003; 9: 621–626.
LiYDuHXieB. Cerebellum abnormalities in idiopathic generalized epilepsy with generalized tonic-clonic seizures revealed by diffusion tensor imaging. PloS One2010; 5: e15219.
17.
KuceyeskiAMarutaJRelkinN. The Network Modification (NeMo) Tool: Elucidating the effect of white matter integrity changes on cortical and subcortical structural connectivity. Brain Connect2013; 3: 451–463.
18.
MufsonEJPandyaDN.Some observations on the course and composition of the cingulum bundle in the rhesus monkey. J Comp Neurol1984; 225: 31–43.
19.
SchmahmannJDPandyaDNWangR. Association fibre pathways of the brain: Parallel observations from diffusion spectrum imaging and autoradiography. Brain2007; 130: 630–653.
20.
PardiniMBonzanoLRoccatagliataL. The fatigue-motor performance paradox in multiple sclerosis. Sci Rep2013; 3: 2001.
21.
SepulcreJMasdeuJCGoniJ. Fatigue in multiple sclerosis is associated with the disruption of frontal and parietal pathways. Mult Scler2009; 15: 337–344.
22.
NiepelGTench ChRMorganPS. Deep gray matter and fatigue in MS: A T1 relaxation time study. J Neurol2006; 253: 896–902.
23.
RoelckeUKapposLLechner-ScottJ. Reduced glucose metabolism in the frontal cortex and basal ganglia of multiple sclerosis patients with fatigue: A 18F-fluorodeoxyglucose positron emission tomography study. Neurology1997; 48: 1566–1571.
24.
GenovaHMRajagopalanVDelucaJ. Examination of cognitive fatigue in multiple sclerosis using functional magnetic resonance imaging and diffusion tensor imaging. PloS One2013; 8: e78811.
25.
DobryakovaEDeLucaJGenovaHM. Neural correlates of cognitive fatigue: Cortico-striatal circuitry and effort-reward imbalance. J Int Neuropsychol Soc2013; 19: 849–853.
PardiniMCapelloEKruegerF. Reward responsiveness and fatigue in multiple sclerosis. Mult Scler2013; 19: 233–240.
28.
MoellerSJTomasiDHonorioJ. Dopaminergic involvement during mental fatigue in health and cocaine addiction. Trans Psychiatry2012; 2: e176.
29.
BlanchardTCHaydenBY.Neurons in dorsal anterior cingulate cortex signal postdecisional variables in a foraging task. J Neurosci2014; 34: 646–655.
30.
FrithCDFristonKLiddlePF. Willed action and the prefrontal cortex in man: A study with PET. Proc Biol Sci1991; 244: 241–246.
31.
ShimaKTanjiJ.Role for cingulate motor area cells in voluntary movement selection based on reward. Science1998; 282: 1335–1338.
32.
YinHHOstlundSBKnowltonBJ. The role of the dorsomedial striatum in instrumental conditioning. Eur J Neurosci2005; 22: 513–523.
33.
HollermanJRTremblayLSchultzW.Influence of reward expectation on behavior-related neuronal activity in primate striatum. J Neurophysiol1998; 80: 947–963.
34.
KimchiEYLaubachM.Dynamic encoding of action selection by the medial striatum. J Neurosci2009; 29: 3148–3159.
35.
VallarGRonchiR.Anosognosia for motor and sensory deficits after unilateral brain damage: A review. Restor Neurol Neurosci2006; 24: 247–257.
36.
CraigAD.Forebrain emotional asymmetry: A neuroanatomical basis?Trends Cogn Sci2005; 9: 566–571.
37.
KuceyeskiAKamelHNaviBB. Predicting future brain tissue loss from white matter connectivity disruption in ischemic stroke. Stroke2014; 45: 717–722.
