Corpus Callosum Index: A practical method for long-term follow-up in multiple sclerosis

Corpus callosum index

References

Pérez-Álvarez AI, Suárez-Santos P, González-Delgado M, Oliva-Nacarino P. Quantification of brain atrophy in multiple sclerosis using two-dimensional measurements. Neurologia. 2018 Jun 8. pii: S0213-4853(18)30152-X. doi: 10.1016/j.nrl.2018.04.004
Figueira, Fernando Faria Andrade, Santos, Valeria Silva dos, Figueira, Gustavo Medeiros Andrade, & Silva, Ângela Correa Marques da. (2007). Corpus Callosum Index: A practical method for long-term follow-up in multiple sclerosis. Arquivos de Neuro-Psiquiatria, 65(4a), 931-935. https://dx.doi.org/10.1590/S0004-282X2007000600001

Corpus Callosum as a biomarker of lobar degeneration

Corpus Callosum as a biomarker for Alzheimer and Multiple Sclerosis

Corpus callosal atrophy and associations with cognitive impairment in Parkinson disease

Methods used for measuring atrophy in Multiple Sclerosis

Location/generic methodology	Method	Advantages	Disadvantages
Brain/linear and regional measures	Third ventricle width	Easy to implement; rapid analysis; standard acquisition methods; enables targeting of eloquent regions	Limited anatomical scope; may miss subtle effects; may exhibit user bias; high user input
	Third ventricle volume
	Brain width
	Corpus callosum width
	Volume on central brain slices
	Stereology
Brain/whole brain segmentation approaches	CSF volumes	Increased automation reduces user bias and user input; generally higher measurement precision	Complex analysis methods; possibly more complex acquisition schemes
	BPF
	WBR
	BICCR
	Fuzzy connectedness
	Probabilistic segmentation (SPM)
	TDS
	SIENAX
Brain/registration‐based methods	MIDAS	Regional atrophy may become apparent	Complex analysis methods; limited application to multiple sclerosis to date
	Voxel‐based morphometry
	SIENA
Spinal cord	Manual outlining	Straightforward	Possible user bias; high user input
	Semi‐automated outline of 3D axial images	Precise
	Automated whole cord volume measurement	Little user input	Complex analysis methods; limited application to date
Optic nerve	Manual outlining	Straightforward	Possible user bias; high user input
	Semi‐automated outline of 3D axial images	Precise
	Automated whole nerve volume measurement	Little user input	Complex analysis methods; limited application to date

TDS = template‐driven segmentation; MIDAS = Medical Image Display and Analysis Software.

Source: David H. Miller, Frederik Barkhof, Joseph A. Frank, Geoffrey J. M. Parker, Alan J. Thompson. Measurement of atrophy in multiple sclerosis: pathological basis, methodological aspects and clinical relevance . Brain 2002. DOI: http://dx.doi.org/10.1093/brain/awf177

Corpus callosum index: a practical method for long-term follow-up in multiple sclerosis

Rather than acute inflammation, long-standing multiple sclerosis (MS) course is hallmarked by relentless axonal loss and brain atrophy, both with subtle clinical expression and scarcely visible on conventional MRI studies. Brain atrophy imaging has sophisticated methodological requirements, not always practical and accessible to most centers. Corpus callosum (CC) is a major inter-hemispheric white matter bundle, grossly affected by long term MS and easily assessed by MRI (1)

CCI is an easy to use MRI marker for estimating brain atrophy in patients with MS. Brain atrophy as measured with CCI was associated with disability progression but it was not an independent predictor of long-term disability (2).

References:

1. Figueira FF, Santos VS, Figueira GM, Silva AC. Corpus callosum index: a practical method for long-term follow-up in multiple sclerosis. Arq Neuropsiquiatr. 2007 Dec;65(4A):931-5
2. Yaldizli O, Atefy R, Gass A, Sturm D, Glassl S, Tettenborn B, Putzki N. Corpus callosum index and long-term disability in multiple sclerosis patients. J Neurol. 2010 Aug;257(8):1256-64

Linear measures for assessing gray matter atrophy in Multiple Sclerosis

The the bicaudate ratio (BCR) is increased in MS and is more closely associated with cognitive dysfunction than are other magnetic resonance imaging surrogate markers including whole-brain atrophy. Increased BCR is best explained by frontal horn ventricular enlargement due to atrophy of deep frontal subcortical white matter. This highlights the close relationship between subcortical atrophy and cognitive impairment in patients with MS (read more) and AD (read more).

Fluid-attenuated inversion-recovery magnetic resonance imaging scan of a patient with multiple sclerosis showing the technique of determining the bicaudate ratio (BCR). The BCR is the minimum intercaudate distance (solid line) divided by brain width along the same line (dashed line).

Bicaudate ratio. The yellow represents the distance between the two apices of the caudate nuclei. The inner skull dimension is shown in turquoise. The bicaudate ratio is derived by dividing the ventricular dimension (yellow) by the inner skull dimension

However, although the bicaudate ratio is a fairly good measure of caudate atrophy, seems to be poor measures of caudate size when no atrophy is present (read more). 

Gray matter imaging in multiple sclerosis: the role of thalamus

Key points: 

• GM damage in MS is common and widespread, especially in chronic MS;
• The underlying pathological correlates of GM damage in MS are different from WM damage;
• GM pathology is present in all stages of the disease, but is more prominent in SPMS and PPMS compared to RRMS;
• Although a relatively non-specific measure of overall pathology, GM atrophy measurements are reliable and robust and correlate strongly with disability and cognitive impairment (more so than WM atrophy);
• Non-neocortical GM damage is frequently detected in histopathological studies as well as on MRI; 
• Thalamic abnormalities have been studied most extensively and were shown to correlate with clinical parameters;

Timeline of GM imaging in MS. A schematic overview of developments in the field of GM imaging in MS from the beginning of the 20^th century until now. Taken from BMC Neurology 2011, 11:153  

Subcortical GM damage in MS. Subcortical atrophy in HC and MS. Above: Effect sizes of subcortical atrophy in a cohort of 120 early RRMS patients, six years post-diagnosis. Below: Two examples of segmented subcortical structures in a healthy control (HC, above) and an age-matched RRMS patient (MS, below). Taken from BMC Neurology 2011, 11:153

Representative FIRST segmentation in a 47-year-old female patient with CIS (EDSS 1.5) on the left and a 47-year-old female patient with RRMS (EDSS 2.0) on the right. The colored regions represent thalamus (green), globus pallidus (dark blue), caudate (light blue), and putamen (magenta). Taken from AJNR 2012 33: 1573-1578

More info:

• thalamus.pdf   Thalamus Tracing Guidelines - W. Ooteman, K. Cretsinger
• Mediodorsal_nucleus_tracing_thalamus.pdf   Mediodorsal Nucleus Tracing Guidelines - S. Ziebel, K. Cretsinger
• Correlation between brain volume loss and clinical and MRI outcomes in multiple sclerosis