Back

Evaluating Quantitative and Functional MRI As Potential Techniques to Identify the Subdivisions in the Human Lateral Geniculate Nucleus

Yildirim, I.; Hekmatyar, K.; Schneider, K. A.

2022-11-16 neuroscience
10.1101/2022.11.16.516765 bioRxiv
Show abstract

Segmenting the magnocellular (M) and parvocellular (P) divisions of the human lateral geniculate nucleus (LGN) has been challenging yet remains an important goal because the LGN is the only place in the brain where these two information streams are spatially disjoint and can be studied independently. Previous research used the amplitude of responses to different types of stimuli to separate M and P regions (Denison et al., 2014; Zhang et al., 2015). However, this method is confounded because the hilum region of the LGN exhibits greater response amplitudes to all stimuli and can be mistaken for the M subdivision (DeSimone & Schneider, 2019). Therefore, we have employed two independent methodologies that do not rely upon the functional response properties of the M and P neurons to segment the M and P regions: 1) structural quantitative MRI (qMRI) at 3T to measure the T1 relaxation time, and 2) monocular and dichoptic functional MRI (fMRI) procedures to measure eye-specific responses. Our qMRI results agreed with the anatomical expectations, identifying M regions on the ventromedial surface of the LGN. The monocular fMRI procedure was better than the dichoptic condition to identify the eye-dominance signals. Both procedures revealed significant right eye bias, and neither could reliably identify the first M layer of the LGN. These findings indicated that the qMRI methods are promising whereas the functional identification of contralateral layers requires further refinement. HighlightsO_LIT1 parameter in qMRI segregates M and P regions of LGN in individual subjects at 3T. C_LIO_LIEye-specific voxels in LGN respond more strongly to monocular than dichoptic viewing. C_LIO_LIClusters of eye-specific regions but not layers can be separated at 1.5 mm resolution. C_LI

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
NeuroImage
813 papers in training set
Top 0.3%
23.5%
2
Brain Structure and Function
83 papers in training set
Top 0.1%
15.4%
3
Human Brain Mapping
295 papers in training set
Top 0.5%
10.9%
4
Imaging Neuroscience
242 papers in training set
Top 0.3%
8.8%
50% of probability mass above
5
Frontiers in Neuroscience
223 papers in training set
Top 0.4%
6.6%
6
Scientific Reports
3102 papers in training set
Top 40%
3.4%
7
eLife
5422 papers in training set
Top 33%
2.5%
8
Aperture Neuro
18 papers in training set
Top 0.2%
1.9%
9
Frontiers in Human Neuroscience
67 papers in training set
Top 1%
1.4%
10
eneuro
389 papers in training set
Top 6%
1.4%
11
NeuroImage: Clinical
132 papers in training set
Top 3%
1.3%
12
Developmental Cognitive Neuroscience
81 papers in training set
Top 0.5%
0.8%
13
Journal of Neuroscience Methods
106 papers in training set
Top 1%
0.8%
14
PLOS ONE
4510 papers in training set
Top 65%
0.8%
15
Frontiers in Neurology
91 papers in training set
Top 5%
0.8%
16
Brain Topography
23 papers in training set
Top 0.4%
0.7%
17
Brain Research
35 papers in training set
Top 2%
0.7%
18
Journal of Cognitive Neuroscience
119 papers in training set
Top 2%
0.7%
19
Communications Biology
886 papers in training set
Top 28%
0.7%
20
Neuropsychologia
77 papers in training set
Top 1%
0.7%
21
The Journal of Neuroscience
928 papers in training set
Top 9%
0.5%
22
Brain and Behavior
37 papers in training set
Top 2%
0.5%
23
Frontiers in Neuroimaging
11 papers in training set
Top 0.6%
0.5%