The future of the human connectome

Abstract

The opportunity to explore the human connectome using cutting-edge neuroimaging methods has elicited widespread interest. How far will the field be able to progress in deciphering long-distance connectivity patterns and in relating differences in connectivity to phenotypic characteristics in health and disease? We discuss the daunting nature of this challenge in relation to specific complexities of brain circuitry and known limitations of in vivo imaging methods. We also discuss the excellent prospects for continuing improvements in data acquisition and analysis. Accordingly, we are optimistic that major insights will emerge from human connectomics in the coming decade.

Figure 1

A. A flat map of cortical areas in the macaque. B. A 32 × 32 binary connectivity matrix for macaque visual areas. Reproduced, with permission, from Felleman & Van Essen (1991).

Figure 2

A map of 52 cortical areas based surface-based analyses of human cerebral cortex and displayed on the fs_LR atlas. Gray areas lack accurate surface-registered maps of well-defined cortical areas. Adapted, with permission, from Van Essen et al. (2011b).

Figure 3

Projection patterns to areas V1, V2, and V4 revealed by retrograde tracer injections in the macaque. Strength of projections are expressed as FLNe (Fraction of Labeled Neurons extrinsic to the injected area) and shown using a logarithmic scale. Adapted, with permission from Markov et al., 2011.

Figure 4

Regional differences in intersubject alignment of probabilistic architectonic maps achieved by FreeSurfer's surface-based registration algorithm (Fischl et al., 2008; Van Essen et al., 2011b). A. Alignment of area 17 in the calcarine sulcus achieves perfect overlap (red = overlap fraction of 1) over most of area 17's extent. B. Overlap for architectonic area hOc5 (putative area MT) in nearby lateral occipito-temporal cortex is nowhere better than 50% (green = overlap fraction of 0.5), and in many regions there is no overlap of the 10 subjects (indigo = overlap fraction of 0.1).

Figure 5

Relative SNR as a function of maximum gradient strength that can be attained by shortening the diffusion encoding time. Theoretical data points are based on a monopolar diffusion encoding scheme for four different b values (assuming instantaneous rise times). The value of b=20,000 s/mm² is likely at the extreme of values to be utilized; current diffusion scans for tractography typically employ b values less than 3000 s/mm². A minimum TE of 15 ms due to RF pulses and partial k-space coverage before the peak of the echo is assumed.

Figure 6

Ratio of 7T SNR relative to 3T SNR for the central k-space point, k_o, calculated for a spin echo sequence (90 deg. excitation and a single 180 deg. refocusing pulse) as a function of te_k1, the time to the first k-space point. 6/8 Partial Fourier EPI acquisition was modeled. The echo time TE is equal to te_k1 plus the time to acquire 2/8 k-space points to reach the ko signal where maximum echo amplitude occurs. Note that for the above graph, TE>0 even when te_k1=0 which accounts for the less than approximately linear gain in SNR at te_k1=0 . T1 of white matter was taken as 0.838 s at 3T (Wansapura et al., 1999) and 1.22 s at 7T (Rooney et al., 2007). Bandwidth was kept the same. The minimum TE was taken as 15 ms. The T2 at 3T was reported to be between 55 and 69 ms (Stanisz et al., 2005 and references therein); for these calculations, it was assumed to be the average of this range, i.e. 62 ms. The 7T white matter T2 was taken as 45 ms (Yacoub et al., 2003). Intrinsic SNR was scaled 1.15 times the magnetic field as previously shown experimentally and by modeling (Vaughan et al., 2001). Same matrix size at 3T and 7T was assumed.

Figure 7

Revolutions in cartography of the earth's surface (left) and the human brain (right).